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Phellem Cell-Wall Components Are Discriminants of Cork Quality in Quercus suber

Cork is a renewable, non-wood high valued forest product, with relevant ecological and economic impact in the Mediterranean-type ecosystems. Currently, cork is ranked according to its commercial quality. The most valuable planks are chosen for cork stoppers production. Cork planks with adequate thickness and porosity are classified as stoppable quality cork (SQC). The chemical composition of cork is known, but the regulation of metabolic pathways responsible of cork production and composition, hence of cork quality, is largely unknown. Here, we tested the hypothesis that post-genomic events may be responsible for the development of SQC and N-SQC (non-stoppable quality cork). Here, we show that combined proteomics and targeted metabolomics (namely soluble and cell wall bound phenolics) analyzed on recently formed phellem allows discriminate cork planks of different quality. Phellem cells of SQC and N-SQC displayed different reducing capacity, with consequential impact on both enzymatic pathways (e.g., glycolysis) and other cellular functions, including cell wall assembly and suberization. Glycolysis and respiration related proteins were abundant in both cork quality groups, whereas the level of several proteins associated to mitochondrial metabolism was higher in N-SQC. The soluble and cell wall-bound phenolics in recently formed phellem clearly discriminated SQC from N-SCQ. In our study, SQC was characterized by a high incorporation of aromatic components of the phenylpropanoid pathway in the cell wall, together with a lower content of hydrolysable tannins. Here, we propose that the level of hydrolysable tannins may represent a valuable diagnostic tool for screening recently formed phellem, and used as a proxy for the quality grade of cork plank produced by each tree.

Cork is a renewable, non-wood high valued forest product, with relevant ecological and economic impact in the Mediterranean-type ecosystems. Currently, cork is ranked according to its commercial quality. The most valuable planks are chosen for cork stoppers production. Cork planks with adequate thickness and porosity are classified as stoppable quality cork (SQC). The chemical composition of cork is known, but the regulation of metabolic pathways responsible of cork production and composition, hence of cork quality, is largely unknown. Here, we tested the hypothesis that post-genomic events may be responsible for the development of SQC and N-SQC (non-stoppable quality cork). Here, we show that combined proteomics and targeted metabolomics (namely soluble and cell wall bound phenolics) analyzed on recently formed phellem allows discriminate cork planks of different quality. Phellem cells of SQC and N-SQC displayed different reducing capacity, with consequential impact on both enzymatic pathways (e.g., glycolysis) and other cellular functions, including cell wall assembly and suberization. Glycolysis and respiration related proteins were abundant in both cork quality groups, whereas the level of several proteins associated to mitochondrial metabolism was higher in N-SQC. The soluble and cell wall-bound phenolics in recently formed phellem clearly discriminated SQC from N-SCQ. In our study, SQC was characterized by a high incorporation of aromatic components of the phenylpropanoid pathway in the cell wall, together with a lower content of hydrolysable tannins. Here, we propose that the level of hydrolysable tannins may represent a valuable diagnostic tool for screening recently formed phellem, and used as a proxy for the quality grade of cork plank produced by each tree.

INTRODUCTION
Cork is a renewable natural product, and its industrial exploitation allows maintain the Quercus suber forests, characteristic landscape of the Mediterranean ecosystem. These ecosystems are designated as "montado" or "dehesa" in Portugal or Spain, respectively. The single out characteristic of Q. suber is the continuous production of a protective cork layer from the phellogen, a secondary meristematic tissue. Chemically, cork consists of both insoluble and soluble components (Pereira, 2007). The insoluble components include aliphatic suberin (often designated as suberin), aromatic suberin (designated as lignin-like), cellulose and hemicelluloses (Silva et al., 2005;Pereira, 2007). Soluble components, or "extractives, " mostly consist of lipids and phenolics. There a is large variability in cork composition, since cork may contain 33-50% aliphatic suberin; 13-29% lignin-like aromatic suberin; 6-25% polysaccharides; 13-24% extractives, and 1-7% ash (Silva et al., 2005). The most abundant extractives are typically waxes and tannins (Silva et al., 2005).
This large variability in the chemical composition coupled with physical properties determines the suitability of cork for the transformation industry hence, its economic value (Lauw et al., 2018). Cork is commercialized as planks, i.e., the rectangular parcels of the outer bark that is separated from the tree along the phellogenic layer without damaging of this meristematic layer that holds the capacity of regeneration right after the debarking process. There is evidence that plank thickness and porosity are major discriminants of cork quality, since stoppable quality cork (SQC) planks ought to display 22-40 mm thickness with limited structural discontinuities (Pereira, 1998). Thicker planks show high cell number per annual ring growth and high cell prism height, i.e., these cells are larger (Silva et al., 2005). The thickness of the cork layer is largely dependent on both genetic and environmental factors, determinant for cell wall thickening development, allowing a more extensive cell expansion and promoting cell division.
There is evidence that the genetic regulation of meristem activity is mostly expressed during spring-summer, when the phellogen becomes more active (Silva et al., 2005;Boher, 2017). For instance, the expression levels of cyclins (promoters of cell cycle progression) were higher in 1-year cork branches of SQC producing trees, whereas in branches producing N-SQC cell expansion is constrained by the up-regulation of genes involved in primary cell wall strengthening (Teixeira et al., 2017). Cork planks, having an elevated number of wider cells, display sustained biosynthesis of cell-wall components, such as suberin-domains and polysaccharides, whose deposition requires biochemical regulation. Currently, the cell wall is described as a sensor, integrating the environmental cues in cell growth (Johnson et al., 2018). Consequently, its biomechanical properties will influence wall flexibility and strength. Recent reports point out to environmental influence on cork annual growth, porosity and thickness (Inácio et al., 2017;Lauw et al., 2018).
Previous transcriptome analysis did not provide clear discrimination between N-SQC and SQC differentiating cells (Teixeira et al., 2014(Teixeira et al., , 2017. It is reasonable to hypothesize that mechanisms operating at post-transcriptional and post-translational levels may have been responsible for modified protein activity. Indeed, post-transcriptional control of phellogen activity via microRNAs has been previously reported (Chaves et al., 2014). Proteomics has the potential to evaluate differences and similarities between cork quality groups. Highly distinctive protein patterns will describe major biochemical differences between cork quality groups, while highly similar protein patterns will support the fine regulations of the phellogen metabolic activity.
Phenolic compounds display multiple roles in plants, including signaling, enzyme activity regulation and structural roles (Weisshaar and Jenkinst, 1998). They also perform antioxidant functions reducing the generation of reactive oxygen species (ROS) and the ROS content once they are formed. N-SQC producing cells were found to have higher soluble phenolics content than SQC: This was associated to stress responses and to the protective role of cork, a hydrophobic barrier against external constraints. It has been hypothesized that N-SQC producing trees may suffer from more severe stressful conditions compared to the SQC counterparts (Teixeira et al., 2014). Therefore, it is important to understand if a higher proportion of soluble phenolics in N-SQC cork producing cells is associated with a lower proportion of cell-wall phenolic compounds.
Our working hypothesis is that, N-SQC producing cells need to invest in soluble phenolics and the metabolic tradeoff is to convey fewer building blocks for suberization. In addition, phenolic compounds have the potential to modulate the cellular oxidative-status, which can be relevant for phellogenic meristematic activity. Favored cell-proliferation and cell expansion over several growing years will have a positive impact on plank thickness, one of the cork quality traits evaluated and valued by the cork industry.

Sample Collection
Samples were taken from five trees producing cork of stoppable quality (SQC) and from five trees producing non-stoppable quality cork (N-SQC). The chosen cork oaks had an estimated age of 50-70 years and were located in southern Portugal. N-SQC was collected in the area of Coruche, Ribatejo (38 • 46 N,8 • 39 W) and SQC at Serra do Caldeirão, Algarve (37 • 15 N, 7 • 59 W). Soil (Cardoso, 1965(Cardoso, , 1974 and climatic data 1 are summarized in Table 1. The classification of cork quality was based on visual observation, taking into account the criteria used by the industry: thickness and homogeneity, porosity and presence of defects (Pereira, 1998(Pereira, , 2007. SQC planks displayed a reduced number of discontinuities and a minimum thickness of 28 mm (before cooking). N-SQC displayed a maximum thickness of 17 mm (before cooking) and contained an elevated number of discontinuities that completely cross the plank.
Samples were obtained by scratching the inner side of freshly removed planks comprising recently formed phellem with suberized (cork) and un-suberized cells (phelloid) and phellogen cells (meristem). Since cell division occurs during spring and summer, the cells in our samples are just a few weeks older. Altogether, samples represented distinct differentiation stages of cork (thereafter referred as to cork producing cells). Immediately 1 www.ipma.pt  (Cardoso, 1965(Cardoso, , 1974. after debarking, samples were collected and snapped frozen in liquid nitrogen. Samples were later on lyophilized, grinded using a ball mill and PTFE shaking flasks (Mikro-Dismembrator S, Sartorius) and stored at −80 • C until further utilization.

Total Phenolics Quantification
Following the protocol used by Zarrouk et al. (2012), 1 mL of acidified methanol (with 1% HCl) was added to 25 mg of sample (dry weight), and incubated at room temperature under gentle agitation in the dark, for the estimation of total soluble phenolics. Samples were extracted for 7, 24, 48, 72, 96, 168, or 216 h in order to optimize the extraction time. After centrifugation (18000 × g for 15 min at 4 • C), supernatants were diluted 1/10 and used for total phenolics quantification, using the Folin Ciocalteau method modified for microplates (Singleton and Rossi, 1965;Zarrouk et al., 2012). Gallic acid was used as standard and the results were expressed in millimoles of gallic acid equivalents per gram of sample dry weight (mmol GAE g −1 DW).
The pellets resulting from 216 h extraction were further used for residues determination, which was achieved by adding 1 mL of acidified methanol (with 1% HCl) and incubating the tubes for additional 48 h. After centrifugation (18000 × g for 15 min at 4 • C), the supernatants were diluted 1/10 and used for residues quantification as mentioned above.

Soluble and Cell Wall Bound Phenolics Quantification by HPLC-DAD and HPLC-MS/MS
Freeze-dried samples (50 mg) were extracted with a methanolwater mixture 80/20 (v/v) pH 2.5 (with formic acid) at room temperature for 24 h under continuous stirring. The solution (containing soluble phenolics) was centrifuged and the supernatant collected. The pellet residues were used for extraction of wall bound phenolics.
The solution was dried under vacuum, the residue re-dissolved in water (pH 2.5), and then extracted three times with diethyl ether. Twenty microliters of the water fraction were injected into a PerkinElmer Flexar chromatograph equipped with a quaternary 200Q/410 pump and LC 200 diode array detector (all from PerkinElmer, Bradford, CT, United States). The diethyl ether fraction was dried and rinsed with MeOH/H 2 O (80/20) pH 2.5; then 40 µL were injected into the HPLC-DAD equipment. Pellet residues previously extracted for soluble phenolics were washed with ethanol and subjected to base hydrolysis, performed by adding 1 N NaOH at 80 • C for 6 h, and then at room temperature for 18 h in test tubes. The supernatant was collected after centrifugation at 8000 × g for 10 min, acidified to pH 2 with 2 N HCl and then extracted three times with an equal volume of ethyl acetate (Blount et al., 2000). In addition, the pellet was washed two times with 3 mL of ethyl acetate. The organic phases were combined, taken to dryness and resuspended in MeOH/H 2 O pH 2.5 with HCOOH (8/2). Twenty microliters of this solution were injected into the HPLC-DAD equipment described above.
In the HPLC-DAD, compounds were separated on a 250 × 4.6 mm i.d (5 µm pore size) RP-C18 Zorbax SB kept at 30 • C. Detection was carried out at 280 and 350 nm. Elution was performed using a linear gradient solvent system, at a flow rate of 1 mL min −1 , consisting of H 2 O (A), CH 3 OH (B) and CH 3 CN (C), all containing 1% of HCOOH. The gradient profile was as follows: 0-2 min 98% A, 1% B, 1% C; 2-52 min from 1% of B and C to 49% of both and then returning to the initial conditions in 5 min. Quantification of individual polyphenols was performed using calibration curves (in 0.001 mg/mL to 0.2 mg/mL concentration range) of authentic standards (all from Extrasynthese, Lyon-Nord, Genay, France): vanillic acid, ferulic acid (ferulic acid, isoferulic acid), gallic acid (for galloyl HHDP glucose and gallotannins), p-coumaric acid for p-coumaric acid itself, caffeic acid (for caffeic acid derivatives), ellagic acid (for vascalagin/castalagin and ellagic acid itself) and epicatechin (for catechin) and expressed as mmol g −1 DW.
The identification of soluble phenolics of cork producing cells was performed through HPLC-MS-MS analysis (diagnostic fragments for each phenolic compound and the molecular ions are provided as Supplementary Table S1). Cell wall-bound polyphenols were assigned to the different classes (gallic acid, vanillic acid, ferulic, p-coumaric, and caffeic acid derivatives) using the comparison of retention time and UV-VIS spectra (Supplementary Table S1) with authentic standards and the data reported in literature (Fernandes et al., 2009;Harris and Trethewey, 2010;Ralph, 2010). Mass spectrometry analysis was conducted with an Agilent LC1200 chromatograph coupled with an Agilent 6410 triple quadrupole MS detector equipped with an ESI source (all from Agilent Technologies, Santa Clara, CA, United States). The column was a Poroshell SB-C18 (2.1 × 100 mm, 2.7 µm). The elution was performed with the same gradient reported above, at a flow rate of 0.3 mL min −1 . Mass spectrometric detection was performed in the negative ion mode after electrospray ionization. The fragmentor was 180 eV. Sample volumes of 3 µL were injected. MS/MS spectra in the negative mode were obtained using argon as collision gas with the collision energy set at 5, 10, and 20 V.
Distribution of the several phenolic compounds detected through polyphenol classes was performed using the Phenol-Explorer database, v3.6 (Rothwell et al., 2013) 2 .

Protein Electrophoresis and MS Analysis
Proteins were phenol extracted, precipitated with ammonium acetate and washed with acetone, in order to concentrate proteins and to remove contaminants that would interfere with the proteomic analysis. One gram of sample dry weight was used for protein extraction, following Isaacson et al. (2006) with minor modifications. Briefly, 10% (w/w) PVPP (polyvinylpolypyrrolidone) was added to the samples and 13.3 mL of extraction buffer was added (0.5 M Tris, pH 7.5; 0.7 M sucrose; 0.1 M KCl; 50 mM EDTA). After brief agitation, 13.3 mL Tris saturated phenol was added and incubated for 30 min on ice. Samples were centrifuged (15000 × g, 4 • C, 30 min) and the upper phase was collected and saved. To the lower phase, an equal amount of extraction buffer was added and after 30 min on ice, samples were centrifuged (15000 × g, 4 • C, 30 min). The upper phases were combined and precipitated overnight at −20 • C with 5 volumes of 0.1 M ammonium acetate in methanol. After centrifugation (20000 × g, 30 min, 4 • C), the supernatant was discarded and the pellet washed with 2 volumes of methanol, gently agitated for 1-2 min and centrifuged (20000 × g, 10 min, 4 • C). The methanol washing was repeated and the pellet further cleaned with 2 volumes of acetone. After 1 h agitation at 4 • C, samples were centrifuged (20000 × g, 10 min, 4 • C). The acetone washes were repeated three more times and after the last centrifugation, the supernatant was discarded and the pellet dried under vacuum (1-2 h). Pellets were resuspended in 8 M urea and 4% CHAPS (w/v), gently agitated for 2 h, centrifuged (9500 × g, 15 min, 25 • C) and the supernatant collected and desalted (PD miniTrap G25) against the same buffer. Protein was quantified with Bradford (Ramagli, 1999). One-hundred micrograms of protein were used for twodimensional electrophoresis. Briefly, isoelectric focusing (IEF) was carried out with pH 3-10 immobilized strips (13 cm) and run to a cumulative 32120 Vh (Pinheiro et al., 2013). To improve the resolution, IEF was performed in the presence of 0.12% DeStreak Reagent (GE Healthcare). The second dimension SDS-PAGE was performed at 20 • C with 12% resolving gels using the Hoefer SE 600 apparatus (GE Healthcare) at 10 mA per gel, for the first 15 min, and 20 mA per gel for the next 4 h, or until the bromophenol blue dye front had run off the gel. Precision Plus Protein All Blue Standards (Bio-Rad, Hercules, CA, United States) were used for molecular mass determinations. Gels were subsequently stained in Colloidal Coomassie Blue (Neuhoff et al., 1985) and images acquired with ImageScanner III (GE Healthcare). Image gel analysis was performed with Progenesis SameSpots 2D software v.4.5 (Non-linear Dynamics Ltd). The automatic analysis assigned 394 polypeptide spots, 355 spots being detected in at least four replicates of each cork quality group (n-1). These spots were submitted to MALDI-TOF/TOF for protein identification.
A Freedom EVO II workstation (Tecan) was used for the digestion. Briefly, gel plugs were washed twice with 50 mM ammonium bicarbonate solution in 50% v/v MeOH/MilliQ Water (Millipore) for 20 min and dehydrated twice for 20 min in 75% ACN. Proteins were digested with 8 µL of a solution containing 5 ng/µL trypsin (trypsin Gold, Promega) in 20 mM ammonium bicarbonate (overnight, 37 • C). Digested peptides were extracted from the gel plugs with 50% v/v ACN containing 0.1% v/v TFA, dried and resolubilized in 0.7 µL of 50% v/v ACN/ containing 0.1% v/v TFA. Peptides were spotted on a MALDI-TOF target and 0.7 µL of 7 mg/mL α-cyano-4-hydroxycinnamic acid in 50% v/v ACN containing 0.1% v/v TFA was added.
A MALDI mass spectrum was acquired using the Sciex 5800 TOF/TOF (Sciex). The 10 most abundant peaks, excluding known contaminants, were automatically selected and fragmented. MS and MS/MS were submitted to an in-house MASCOT server (version 2.3.1; Matrix Science 3 ) for databasedependent identifications against the NCBInr database limited to the taxonomy Quercus (taxID3511; from February 01, 2019; 1,078,293 sequences). A second search was performed using the EST oak sequences from NCBI (taxID3511; from February 01, 2019; 870,474 sequences). Parameters were: peptide mass tolerance 100 ppm, fragment mass tolerance 0.5 Da, cysteine carbamidomethylation as fixed modification (alkylation was performed during the equilibration step between IEF and second dimension), and methionine oxidation, double oxidation of tryptophan, tryptophan to kynurenine and ethylation of glutamic acid as variable modifications. Kynurenine, resulting from tryptophan oxidation, is an artifact often observed during automatic digestion in our laboratory. Ethylation of glutamic acid is an artifact resulting from gel staining and destaining procedures. Up to two miscleavages were allowed. An identification was considered significant when at least two peptides passed the Mascot threshold score, or a single peptide passed twice this score. Manual checking of the spectrum allowed to confirm some identifications with slightly lower scores. When high-quality spectra were not matched to a protein, manual interpretation of the spectra was performed and/or the search parameters adjusted (semitryptic, single amino acid changes, post-translational modifications) to increase the sequence coverage of the identified protein. In our dataset, and regarding post-translational modifications, only acetylation was found, pointing out to a more technical than biological related modification. All identifications were validated manually. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium 4 via the PRIDE partner repository (Vizcaíno et al., 2016) with the dataset identifier PXD014398.
For 45 protein spots, no MS spectra was obtained. Match with database sequences was found for 310 protein spots. Single, and significant, protein match was detected for 215 spots. For 69 polypeptide spots, and although at least one secure protein identification was obtained, the presence of other proteins was evident. Most of these protein spots (n = 66) displayed two matches. In several cases (11 spots), matches were achieved but no functional annotation was available. A bioinformatics analysis allowed retrieving the protein identity for these spots.
For each differentially expressed protein spot a manual annotation process was carried out. The peptide pool of each spot was scanned for uniqueness, and collinearity. Sequences sharing common sub-sequences were consolidated by concatenation prior to similarity search. The similar candidates were obtained through standard BLASTP (Boratyn et al., 2013) from amongst the plant entries in the NCBI Protein database (Agarwala et al., 2016). Only the top scoring hit in each comparison was retained for further analysis. This was true provided a reasonable balance between the degree of overall similarity and the significance (e-value) of the match. A flexible threshold policy was followed to accommodate the variation in length of the query peptides. Any existing InterPro (Mitchell et al., 2019) annotation in the target entries, and for the matched locations was transferred for the query. Whenever feasible, the pool of peptides obtained for each source protein was aligned to a known related sequence in order to sort them out along the original sequence. This was achieved using EMBOSS:needle (Rice et al., 2000) with BLOSUM35 substitution matrix, no end-gap penalties, and avoiding the insertion of indels in the alignment.

Annotation of Peptides From the Whole Data Set
The whole set of spots, and their sequenced peptides was processed through a pipeline of automatic annotation that followed, roughly, the manual annotation already described. This process ensured that only relevant functional annotation was transferred to those peptides. All obtained peptide sequences were matched against UniProt entry sequences (The UniProt Consortium, 2019) using BLASTP (Boratyn et al., 2013). The matches were subsequently mapped on InterPro features already annotated on each of the matched UniProt entries. From the latter it was possible to transfer InterPro federated annotation like GO node references (The Gene Ontology Consortium, 2019), and EC activities (Nagano, 2005). The mapping of the UniProt matches onto their precise InterPro annotation pointed toward 524 corresponding entries in the latter database. These in turn, were associated to 111 Gene Ontology terms and 90 entries in the EzCatDB database 5 . The individual functional annotation of each peptide revealed at least 13 spots with more than one annotated function. The individual annotation of each peptide enabled us to avoid the common issue associated with the results of plain similarity searches -contamination by the annotation associated with unrelated parts of the subject sequence. After functional peptide annotation, they were clustered according to their parent protein spots.
Some classes of functional annotation were related to a wide scope of proteins, and the gel spots assigned to them are bound to lack in specificity for their role or localization in the studied cells. In order to be able to annotate them in a more specific subset of those functional annotation classes, a BLASTP (Boratyn et al., 2013) search against the NCBI Protein database FIGURE 1 | Folin-Ciocalteu based quantification of the total soluble phenolic compounds extracted from stoppable quality cork (SQC) and non-stoppable quality cork (N-SQC) producing cells. Extraction times between 7 h and 216 h were assayed. The remaining soluble phenolics after 216 h of extraction were also quantified. Data show the means ± standard error (n = 5). Significance levels between cork quality groups were assessed via Mann-Withney U-test ( * p < 0.05; * * p < 0.01). (Agarwala et al., 2016) was conducted for some pertinent spots. The resulting matches were assessed for convergence on either activity or localization. Only relevant results were retained.
The location inside the cell was derived either from the InterPro GO annotation or from related protein similarity or phylogenetic analysis.  (Thioulouse and Dray, 2007).

Data Analysis
In order to integratively analyze quantitative metabolite and protein data, a z-transformation was applied to multivariate statistics (PCA and Cluster heatmap-Pearson analyses), using the MATLAB (R2017a, the MathWorks, Inc., Natick, MA, United States.) package software tool COVAIN (v. 2019) (Sun and Weckwerth, 2012).

Cork Quality Discrimination by the Soluble and Immobilized Phenolic's of the Cork Producing Cells
Total phenolics, as estimated with the Folin-Ciocalteau method, were more abundant in N-SQC than in SQC, regardless the extraction time (Figure 1). However, this method typically overestimates soluble phenolics content, since it detects also a range of reducing compounds, such as ascorbate and reducing sugars (Georgé et al., 2005;Seruga et al., 2011). Therefore, the method is a better proxy for the reducing capacity of the sample (to which phenolics contribute), showing that N-SQC had a higher reducing capacity than SQC (around 40%).
Single phenolic compounds were quantified with HPLC-DAD. A higher content of soluble phenolic compounds was found in N-SQC producing cells (fourfold higher) than in SQC producing cells. As expected, HPLC-DAD analysis revealed a much lower content in total soluble phenolics compared to the Folin-Ciocalteau quantification method (it represents circa 3.3 and 1.2%, for N-SQC and SQC, respectively, Supplementary Figures S1A,B).
The soluble phenolics fraction comprised two main phenolic classes: hydrolysable tannins and hydroxybenzoic acids. Hydrolysable tannins were the main component in both N-SQC (96%) and SQC (90%, Figure 2). However, hydrolysable tannins content was much higher in N-SQC than in SQC producing cells (40.03 vs. 8.95 mmol g −1 DW, respectively). N-SQC and SQC cells also differed for the main hydrolysable tannins: monogalloyl-glucose, the main component in N-SQC (around 56% of all phenolics detected) while in SQC, the isomers castalagin, vescalagin and their derivatives were the main compounds (∼70% of all phenolics detected). Hydroxybenzoic acids (ellagic acid, gallic acid, valoneic acid-dilactone), the second most represented class, were also more abundant in N-SQC than in SQC producing cells (1.35 mmol g −1 DW vs. 0.18 mmol g −1 DW, respectively). On the other hand, the amount of flavanol catechin and the hydroxy-cinnamyl-aldehyde coniferaldehyde in SQC producing cells was much higher (8.6%) when compared to N-SQC samples (0.8%). Catechin is the building block of condensed tannins, but in our dataset this class of phenolics was not detected.
On the other hand, cell wall-bound phenolics were 2.25 times more abundant in SQC than in N-SQC producing cells (Figure 3), albeit the lack of differences between the FIGURE 2 | Quantification of soluble phenolic compounds extracted from stoppable quality cork (SQC) and non-stoppable quality cork (N-SQC) producing cells using HPLC-DAD. (A) Hydrolysable tannins. (B) Hydroxybenzoic acids belonging the class of phenolic acids (ellagic acid, gallic acid, and valoneic acid-dilactone), flavanols belonging to the flavonoids class (catechin) and hydroxycinnamaldehydes belonging to other polyphenols class (coniferaldehyde). Polyphenol classes and sub-class as described in the Phenol-Explorer database. Data show the means ± standard error (n = 5). Significance levels between cork quality groups were assessed via Mann-Withney U-test ( * * p < 0.01).
type of phenolics detected. The cell wall bound phenolics consisted of 90% of hydroxycinnamic acids (ferulic, isoferulic, p-coumaric, and caffeic acid derivatives) and about 6% of hydroxybenzoic acids (vanillic acid derivatives). An unknown compound representing 4% of the cell wall-bound phenolics was also detected (Figure 3).
The PCA of the phenolic compounds (both soluble and cell wall-bound) allowed to clearly discriminate between SQC and N-SQC producing cells along the 1st component (77% of variation explained, Supplementary Figure S1C). Several metabolites involved in the phenylpropanoid pathway, including caffeic acid, ferulic acid and vanillic acid were positively related to SQC producing cells. In contrast, several metabolites of the hydrolysable tannin pathway were found positively related with N-SQC producing cells. The exceptions were catechin and coniferaldehyde that positively discriminated SQC.

Cork Producing Proteome
Stoppable quality cork and N-SQC producing cells did not differ in the amount of solubilised protein (Supplementary Figure S2).

FIGURE 3 | Quantification of immobilized phenolic compounds extracted from stoppable quality cork (SQC) and non-stoppable quality cork (N-SQC) producing cells after alkaline hydrolysis. (A)
Major components are phenolic acids of the sub-class hydroxycinnamic acids (derivatives of ferulic acid, isoferulic acid, and caffeic acid). Six isoform candidates of caffeic acid were found and the quantification for each isoform is available as Supplementary  Table S2. (B) Minor components are hydroxycinnamic acids (p-coumaric acid derivative) and hydroxybenzoic acids (vallinic acid derivative), hydrolysable tannin (galloyl-HHDP-glucose) and an unknown compound putatively assigned as a ferulic acid derivative (CWB_unknown). Polyphenol classes and sub-class as described in the Phenol-Explorer database. Data show the means ± standard error (n = 5). Significance levels between cork quality groups were assessed via Mann-Withney U-test ( * * p < 0.01).
The two-dimensional gel-based proteomics approach allowed the detection of 355 reproducible polypeptide spots, and these spots were found in both cork quality groups.
The high quality functional annotation of peptides described in the section "Materials and Methods" allowed us to retrieve the biological processes most represented in cork producing cells (Figure 4). These cells are characterized by possessing high glycolysis and energy production (37%) and amino acid metabolism, protein synthesis and turnover (39%), which is compatible with actively growing phases of the cell cycle. Considering the number of putative isoforms detected, the most represented enzymes involved in aerobic respiration were ATP synthase (EC 3.6.3.14), enolase (EC 4.2.1.11), fructose-bisphosphate aldolase (EC 4.1.2.13) and glyceraldehyde-3-phosphate dehydrogenase (EC 1.2.1.12). Enzymes involved in amino acid metabolism were detected, with a large preponderance of S-adenosylmethionine synthetase (EC 2.5.1.6) detected as 16 putative isoforms. Proteins involved in pyrimidine and purine nucleoside triphosphates biosynthesis, the activated precursors of DNA and RNA, were also largely represented (16 putative isoforms) along with proteins involved in protein turnover via the proteasome pathway (14 putative isoforms).
Fatty acid metabolism and lignin and lignan biosynthesis represented 13.4% of the annotated proteome, suggesting an active synthesis of secondary cell wall components. This percentage could be higher as six additional acyl-carriers proteins were identified (fatty acid metabolism) but the peptide annotation did not allow to securely add them to the proteome. Lignan biosynthesis was represented by proteins with NmrA-like domains (IPR008030) matching isoflavone reductase. Lignin biosynthesis was represented by caffeoyl-CoA O-methyltransferase, which belongs to the superfamily SAMdependent O-methyltransferase (IPR029063). This enzyme is involved in the synthesis of feruloylated polysaccharides and in the formation of cell wall-bound ferulic acid polymers (i.e., secondary cell wall assembly and defense response). ROS metabolizing enzymes (catalase and peroxidases) represented 9% of the annotated proteome.

Cork Quality Discrimination by the Proteome of Cork Producing Cells
A PCA analysis of the proteome discriminated the cork quality groups (Supplementary Figure S2D) with 16% and 14% of the variation being explained along the 2 nd and 3 rd PCA axis. The integrative analysis of proteome and phenolic data was suitable to discriminate the examined cork quality groups (PC1 68%, Figures 5, 6). In detail, the groups largely differed for the phenolics component and less for the proteome component. Only 14 protein spots, a small fraction of the 355 spots detected (ANOVA statistics, Figures 5, 6 and Table 2) allowed discrimination. Unique protein identification was available for only 10 spots ( Table 2 and  Supplementary Table S3). Consistently, only a small fraction of the polypeptide spots differentially accumulated in cork quality groups (Supplementary Figure S2 and Supplementary  Table S3). Similarly to previous reports (Pinheiro et al., 2013), the assessment of a given polypeptide spot as differently expressed accumulated was univariate test dependent. In the present work, we considered a gel spot signal to be distinct between cork quality groups when simultaneously assessed by the two non-parametric tests (p < 0.05 for MW; p < 0.08 for KS). In the proteome data set, 22 protein spots fulfilled this criterion with unique identification being achieved for 16 spots (Supplementary Table S3). This list largely overlapped with protein spots ascribed as discriminators between cork quality groups through the integrative proteomics and phenolic analysis ( Table 2). Exceptions were verified for protein spots 425 (no protein identification achieved) and for 1777 (identified as cyclophilin). Altogether, our analysis identified 18 proteins as having quantitative differences between the quality groups, 14 of them having bigger volumes in N-SQC.
One putative isoform of cyclophilin (spot 1777) was 35% more abundant in N-SQC. This was only one of four candidate cyclophilin isoforms and when all the presumed isoforms were considered, cyclophilin was found 53% more abundant in N-SQC producing cells.
FIGURE 4 | Relative distribution of proteins by reaction/process. The set of activities found for each spot after functional annotation was expertly assessed for relevant cellular reactions or processes, and here they are depicted by representation in terms of spots found. The glycolysis slice was further expanded for the sake of clarity on its individual component representation.
Three isoforms candidates of isoflavone reductase were significantly more abundant in N-SQC, altogether representing 50% more volume when compared with SQC. Considering all the nine putative isoforms detected in our dataset, N-SQC producing cells exhibited 20% more volume of isoflavone reductase. These proteins were included in the lignan biosynthesis category due to its identification as isoflavone reductase that catalyzes the NADPH-dependent reduction of phenylcoumaran benzylic ethers. However, the detailed peptide analysis only confirms the identification of NmrA-like family domain containing proteins (IPR008030), described as negative regulators of the transcription factor AreA. NmrA-like proteins are a subgroup of the NAD(P)-binding domain (IPR016040) that overlaps with the NAD(P)-binding domain superfamily (IPR036291). Among the 33068 proteins that present the NmrA-like domain, 65 are from Viridiplantae -Arabidopsis and Oryza 6 . This superfamily includes many reductases (e.g., isoflavone reductase homolog P3 or pinoresinol -lariciresinol reductase 3 from Arabidopsis) and, in plants, this function seems to overlap with the role of negative regulator of transcription that is recognized in fungi.
One isoform of glutathione S-transferase dehydroascorbate reductase-like (spot 1354), was significantly more abundant in SQC (15 %). Considering all seven putative isoforms detected, no quantitative changes were found between the cork quality groups.
The protein detected in spot 1163 (40% more abundant in N-SQC) was identified as calcium-binding protein. Additionally, in three other spots, proteins were also identified as calciumbinding protein. These proteins share the EF-hand domain (IPR002048), which is detected in 473 and 344 proteins, 6 http://www.ebi.ac.uk/interpro/entry/IPR008030/proteins-matched respectively, in Arabidopsis and rice. The EF-hand motif is an evolutionary related calcium-binding domain exhibited by many calcium-binding proteins 7 . The biological role is diversified and the lack of additional information only permits to consider a potential calcium sensor role.
One putative isoform of annexin (spot 1044), a calciumdependent phospholipid-binding protein, was differently abundant between the cork quality groups (40% more spot volume in N-SQC). When considering all three isoforms detected, N-SQC producing cells exhibit 30% more of this protein. Recent reports suggest multiple roles for annexin, including signal transduction, exocytosis and endocytosis and organization of the extracellular matrix 8 .
Spot 1870 was found to be differently accumulated in N-SQC (45%), the protein being identified as RNA-binding protein. This protein exhibits the RNA recognition motif domain (IPR000504), the same motif was detected within five other spots. Taking into consideration their similarity, three different proteins were most likely detected. Proteins of spots 1870, 1905, and 1928 were seen as potential isoforms and when considered together, they were significantly more abundant in N-SQC than in SQC (30% more). Proteins from spots 1694 and 1697 were also regarded as potential isoforms and were also detected in higher abundance in N-SQC (25%). A similarity analysis conducted against proteins with the same functional annotation pointed toward mitochondria as their subcellular location. Proteins with this RNA recognition motif domain are described to play different roles including binding to heterogeneous nuclear ribonucleoproteins, small FIGURE 5 | Integrative PCA of significantly different metabolite and protein abundance levels (ANOVA p < 0.05, n = 5, z-transform data, using COVAIN), distinguishing cork quality groups. Only significant variables are shown. All the phenolics were significantly associated with a cork quality group. The PCA loadings were used to build the plot (x-and y-axis).
nuclear ribonucleoproteins, regulation of alternative splicing and regulation of RNA stability and translation. Polyadenylatebinding proteins are also RNA binding proteins, and one isoform was found to be more abundant in N-SQC (40%, spot 724). This protein was detected in four spots and all to be considered putative isoforms with no quantitative changes between the cork quality groups found.
One regulatory subunit of the 26S proteasome (superfamily IPR035263) was found to be differently abundant, showing a two-fold increase in N-SQC producing cells. This regulatory subunit is an ATPase with an AAA + domain. The AAA + superfamily of ATPases is involved in protein processing, transport and degradation 9 .
Two isoform candidates for ATP synthase were found to accumulate 30% more in N-SQC than in SQC. Additional 7 possible isoforms were identified as ATP synthase, and when all potential isoforms were considered in N-SQC producing cells, it exhibited 15% more of this protein. Once again, the similarity analysis pointed out for mitochondria as the most probable subcellular location for these isoforms candidates. The transport 9 http://www.ebi.ac.uk/interpro/entry/IPR003593 across the mitochondria outer membrane via porins also showed differences between the cork quality groups. Several porins were detected in our dataset, and a detailed analysis indicated the presence of the domain IPR027246. This feature supports a role as a translocator channel (Tom40) for unfolded protein across the outer mitochondrial membrane. One Tom40 isoform was found to be differently expressed (40% more abundant in N-SQC, spot 1220). The same trend was observed when all the ten putative isoforms were considered, with N-SQC producing cells exhibiting 25% more of this protein translocator.
Detailed peptide analysis allowed to confirm the identity of spots 1687 and 1709 as an acyl-carrier proteins (ACP), the volume of spot 1687 being 30% higher in SQC. Several other acyl-carrierproteins (reductases and dehydrases) were identified. When the volume of the four acyl-carrier proteins was considered, no quantitative changes were found between the cork quality groups. These proteins function as cofactors in the de novo synthesis of fatty acids major constituents of suberin, cutin, and cellular membranes (Huang et al., 2017).
Stoppable quality cork also showed higher abundance of one Caffeoyl-CoA O-methyltransferase putative isoform (25% higher, spot 2856). But, when the volume of the five FIGURE 6 | Integrative hierarchical cluster analysis of all statistically significantly changed metabolites and proteins (ANOVA, p < 0.05, n = 5, using COVAIN). Blue color means variables accumulated in N-SQC; red color indicates accumulation of compounds of SQC. All the phenolics were significantly associated with a cork quality group.
isoform candidates detected were considered, no quantitative changes could be found between the cork quality groups. Caffeoyl-CoA O-methyltransferase (EC 2.1.1.104) is a S-adenosyl-L-methionine-dependent enzyme (IPR029063) involved in the synthesis of different monolignols which are components of lignin (Zhong et al., 2000). Similarly to caffeoyl-CoA O-methyltransferase, no quantitative changes were found between the cork quality groups for S-adenosylmethionine synthetase (involved in cysteine and methionine metabolism as well as in the biosynthesis of secondary metabolites).
One presumed isoform, 2,3-bisphosphoglycerateindependent phosphoglycerate mutase, an enzyme involved in glycolysis, was assessed as differently accumulated in SQC (40 % higher volume, spot 513). But when considering the volume of all the three isoform candidates, no quantitative changes were found between the cork quality groups. Also, when all the glycolytic enzymes (and its several putative isoforms) were considered, no quantitative changes were found between the cork quality groups, the same trend was observed for the ROS metabolizing enzymes.

DISCUSSION
At the debarking, the proteome of the Q. suber cork producing cells showed high demand for energy and metabolic building blocks (carbon and amino acids; Figure 4), as previously observed by Ricardo et al. (2011). Quercus proteomes are largely under-represented in the literature (fewer than 25 papers by March 2013; Pinheiro et al., 2014) and to the best of our knowledge, only our study and that of Ricardo et al. (2011) have analyzed the cork producing proteome literature survey in December 2018 using the same sources and search criteria as Pinheiro et al. (2014).
The direct comparison of the proteome (this study) and the transcriptome (Teixeira et al., 2014) of cork producing cells showed differences between the two scales of analysis (Supplementary File S1 and Supplementary Figure S3), namely for the molecular function. The functional annotation of the proteome and transcriptome dataset showed a large preponderance of enzymes (catalytic activity) and binding. Proteome analysis, but not the transcriptome analysis, revealed high abundance of oxidoreductases (26%) and metal ion binding (16%) proteins The enzymes annotated under these functional categories included several dehydrogenases and enolases (involved in glycolysis and respiration) and signaling related proteins, such as annexins and other Ca 2+ binding proteins.
Our study showed that proteome of recently formed suberized and un-suberized phellem, in combination with qualiquantitative analysis of soluble and cell wall-bound phenolics, can be used as proxy for the quality grade of the cork plank. Considering the relative costs of analysis, phenolics seems to provide a more accessible proxy of cork quality. Our study, showed that SQC producing cells made greater investment to the synthesis of cell wall-bound phenolics compared to N-SQC cells. It also revealed a large association of hydroxycinnamic acids and ferulate esters to cell wall polymers (via covalent bonds). The extent of ferulate oxidative cross-linking has been reported to influence the extensibility of cell wall and, consequently not only cell growth, but also pathogen accessibility (Mathew and Abraham, 2004). Consistently, our results, especially the content of coniferaldehyde, which represents a branch point of the biosynthesis of coniferyl and sinapyl alcohol (Pereira, 2007;Marques et al., 2016), was higher in SQC than in N-SQC, thereby indicating a more active lignin biosynthesis. On the contrary, N-SQC producing cells invested mostly in the biosynthesis of soluble phenolics, mainly hydrolysable tannins, consistently exhibiting a higher reducing capacity (more 40% compared to SQC producing cells; Figures 1, 2). Soluble phenolic compounds can play multiple cellular functions, from free radical scavenging (antioxidants) to the regulation of enzyme activity. Hydrolysable tannins (gallic acid esterified with sugars and forming oligomers or polymers) are effective antioxidants (Santos et al., 2010;Khoddami et al., 2013), with strong impact on ROS homeostasis and redox sensing, and hence, on the integration of external stimuli (Mhamdi and Van Breusegem, 2018;Waszczak et al., 2018). In addition, hydrolysable tannins have a role in the crosslinking and aggregation of proteins, contributing to biotic defense via anti-microbial activity (Dixon et al., 2002;Vogt, 2010). In our study, quantitative, but not qualitative differences were observed in both proteome and phenolic metabolism, therefore suggesting that the distinct cork quality is related to a fine metabolic pathway regulation. We speculate that cells integrating external stimuli to synthesize soluble hydrolysable-tannins at expenses of cell wallbound phenolics, produce N-SQC, though the matter deserves further investigation. The significance of phenolic metabolism on cork quality was further illustrated by group discrimination achieved when using protein, phenolic compounds or a combination of both (30% vs. 86% vs. 79%, respectively; Supplementary Figures S1C, S2D and Figure 5). The lower discrimination driven by proteomic analysis was likely due to low proteome polymorphism, since differences in the proteome were quantitative. Additionally, less than 10% of detected protein spots showed significant changes between quality groups. Our study revealed mitochondrial metabolism as one of the most distinctive feature of different quality groups, at the proteome level. Indeed, the abundance of several proteins with putative mitochondrial location was higher in N-SQC: these included ATP synthase, RNA-binding proteins, and unfolded protein translocation to the mitochondria, i.e., import of proteins synthetized de novo via Tom40, a membrane protein essential for import of protein precursors into mitochondria.
Albeit we have used dry weight as unit basis, equivalent number of cells couldn't be assessed in the present work. In addition, cell wall thickness per cell also emerged as a relevant trait. It is, therefore, necessary to establish the correlation between cell division and elongation, as well as between cell wall composition and thickness. Microscopic evaluation of cell shape and dimension could provide useful information on G × E effects on cork growth. Cell number (phellogen activity) and cell height (cellular expansion) are under genetic control (Powell and Lenhard, 2012;Miguel et al., 2016) but are also strongly dependent on environmental conditions (Pereira, 2007). The local combination of temperature and soil water availability largely impacts cork growth (Costa et al., 2016), with differences in cork annual growth being observed between and within geographical regions (Lauw et al., 2018). The basis for such phenotypic plasticity relies on both cork oak genetic diversity and local adaptations (Elena-Rossellö and Cabrera, 1996;Coelho et al., 2006;Modesto et al., 2014;Eriksson, 2017;Lauw et al., 2018).
Our sampling occurred in two distinct geographical areas and, therefore, our data reflected G × E interactions. Trees in each "montado" originated from spontaneous sowing and most likely share the same genetic pool. As a consequence, the impact of environmental factors on both population fitness (evolutionary record) and individual performance (short-term effect) should influence the cork quality.
It is suggested that the analysis of recently formed phellogen from individuals grown in the same geographical area, with known cork quality production and genetic provenance, is crucial to solve the complex matter. This should allow to specifically disclose cork-quality related traits in the same environment and to further test the relative abundance of soluble hydrolysable tannins and cell-wall immobilized phenolics and how they related with cork-quality groups.

CONCLUSION
Cork producing cells show a large preponderance of glycolysis, respiration and protein synthesis and degradation pathways, consistent with their meristematic nature. This observation is compatible with active cell growing phases, in which many proteins are produced and degraded according to the cell cycle phase. Active growth is further supported by a high respiratory demand. Redox regulation is also found relevant in cork producing cells, having the potential to regulate enzyme activity (e.g., glycolysis) and several other cell functions, including cell wall assembly and suberization. SQC and N-SQC producing cells exhibit similar protein patterns, suggesting fine regulations of the phellogen metabolic activity. One possible mechanism is through post-translational modification of the phellogen proteins, with impact on the meristematic activity, cell expansion and differential cell wall immobilization of phenolics. SQC producing cells exhibit a higher amount of cell wall-bound phenolics (hydroxycinnamic acids and hydroxybenzoic acids), which contrasts with N-SQC producing cells. N-SQC not only exhibits a higher amount of hydrolysable tannins but also differs in the type of most abundant component. Secondary metabolites confer a better hint to where we might find the actual key control mechanisms. In our study, distinct patterns of immobilized and soluble phenolics were observed concurrent with highly similar proteomes. We hypothesize that the observed differences in cork quality originates from: (i) regulation and activity of the enzymes involved in cell wall assembly and extracellular matrix dynamics, with strong impacts on cork thickness (ii) higher synthesis, transport and exocytosis of the cell wall components. The possibility to discriminate between SQC and N-SQC at molecular level provides a valuable diagnostic tool. Our study highlights hydrolysable tannins as promising metabolic markers. The validation of these metabolites would be very useful for the screening of individuals, especially if used early in selection.

DATA AVAILABILITY
All datasets for this study are included in the manuscript and the Supplementary Files.

AUTHOR CONTRIBUTIONS
CP, CR, and RT designed the project. RT selected the trees and collected the samples. CP, MT, and CB supervised the protein and metabolic extraction as well as data analysis. SP and JR were responsible for the proteomic mass spectrometry. OZ, MT, CB, and AG performed the phenolic extraction and HPLC-MS/MS analysis. CP, SP, CB, JdA, and SW were involved in data curation, validation, and analysis. CP, CR, and RT were involved in funding acquisition. CP, CR, MT, JR, and SW provided the necessary resources. All authors contributed to the manuscript writing, and read and approved the final manuscript.

ACKNOWLEDGMENTS
The Q. suber samples were kindly provided by two producers associations: Associação de Produtores Florestais do Concelho de Coruche e Limítrofes (APFC) and Associação de Produtores Florestais da Serra do Caldeirão. Adelaide Machado is acknowledged for their valuable technical support.


extfieldsofstudy:
Medicine
Materials Science

text:
Light-sheet microscopy of cleared tissues with isotropic, subcellular resolution.

We present cleared tissue Axially Swept Light-Sheet Microscopy (ctASLM), which enables isotropic, subcellular resolution, high optical sectioning capability, and large field of view imaging over a broad range of immersion media. ctASLM can image live, expanded, and both aqueous and organic chemically cleared tissue preparations. Depending on the optical configuration, ctASLM provides up to 260 nm axial resolution, an improvement over confocal and other reported cleared tissue light-sheet microscopes by a factor 3–10. We image millimeter-scale tissues with subcellular 3D resolution, which enabled us to automatically detect with computer vision multicellular tissue architectures, individual cells, synaptic spines, and rare cell-cell interactions.

Human tissues are composed of multiple polarized cell types organized in well-defined three-dimensional architectures. Interestingly, it has been shown that rare subsets of cells play a crucial role in disease progression, 1 and interdisciplinary efforts now aim to generate comprehensive atlases of human cells in diverse tissue types. To date, this has largely relied on massively parallel sequencing and machine learning-based analyses to identify unique sub-populations of cells. Combined with advanced imaging, such efforts could not only shed light on the diversity of cell types, but the biological context in which each population operates. However, imaging large tissues with subcellular resolution remains challenging due to the heterogeneous refractive index and composition of tissues, which results in complex aberrations and an increased scattering coefficient, both of which decrease spatial resolution and limit imaging depth. 2 To circumvent this challenge, a large variety of optical clearing techniques have been developed that aim to homogenize the optical properties of the tissue using aqueous or organic solvents. 3 Today, these clearing techniques routinely render biological specimens sufficiently transparent, such that -if the imaging technology were to exist -entire organisms could be imaged with subcellular resolution and with molecular specificity. As the specimens are three-dimensional (3D), an ideal imaging system should possess isotropic resolution, in order to accurately measure 3D cellular morphology, biomolecular localization, and signaling activity. 4 Nevertheless, imaging chemically cleared specimens with diffraction-limited or super-resolution presents technical challenges. For example, each clearing mechanism provides advantages and disadvantages, and requires unique immersion media with refractive indices that range between 1.33 and 1.559. Thus, the imaging system must optimally operate throughout this refractive index range without suffering from deleterious aberrations, which decreases both resolution and sensitivity.
Light-sheet fluorescence microscopy (LSFM), because of its parallelized image acquisition, inherent optical sectioning, and ability to image large biological structures quickly and with high optical resolution, serves as an ideal candidate for cleared tissue imaging (Supplementary Table 1). 5 Nevertheless, to the best of our knowledge, there is not yet a light-sheet microscope for cleared tissue specimens that possesses submicron, isotropic resolution, and is also compatible with the full range of clearing methods. [6][7][8][9] Although Lattice light-sheet microscopy readily achieves high spatial resolution, it relies on specialized high numerical aperture lenses that are only compatible with aqueous solvents (refractive index = 1.333). Here, we address these limitations, and present a scalable imaging platform that provides sub-cellular anatomical detail in any spatial dimension across millimeter cubes of tissue. The system, which we refer to as cleared tissue Axially Swept Light-Sheet Microscopy (ctASLM), builds upon previous work where the sample was mechanically scanned through a thin light-sheet. 10,11 Instead of slow sample scans, ctASLM scans the illumination in its propagation direction using high-speed aberration-free remote focusing (Supplementary Note 1, Supplementary Figure 1), refractive indexindependent illumination and detection optics, residual aberration correction, and syncronous camera readout to deliver isotropic high-resolution, large field of view, and highcontrast imaging (Figure 1a). 12,13 Compared to other light-sheet variants that lack aberration free refocusing (such as mesoSPIM) and camera synchronization (such as tiling light-sheet microscopy), ctASLM is better suited for using thin light-sheets over large field of views (Supplementary Note 1). 14,15 Consequently, we present two implementations of ctASLM using matched multi-immersion objectives of NA 0.4 or 0.7, which provide isotropic subcellular imaging at two resolution levels (~600 and ~300 nm, respectively, Supplementary To evaluate optical performance, we imaged sub-diffraction beads with ctASLM, conventional light-sheet microscopy and an Airy Scan confocal microscope (Figure 1b), each equipped with long working distance multi-immersion objectives (detailed Full-Width Half-Maximum values are given in Table 1 in the online methods). Importantly, ctASLM improves axial resolution sixfold over conventional LSFM and three-to seven-fold over Airy scan confocal microscopy. Depending on the refractive index of the immersion media, ctASLM achieves an isotropic, raw resolution of 800-900 nm with the NA 0.4 objectives and 400-500 nm with the NA 0.7 objectives, respectively, over the same field of view encompassed by conventional light-sheet microscopy ( Figure 1b 17,18 Iterative deconvolution improves the resolution of ctASLM by ~30%, leading to 300 nm resolution with the NA 0.7 objective (Table 1 online methods, Supplementary Figures 6 and 7), which we further confirmed inside a biological sample (Supplementary Figure 8). To the best of our knowledge, ctASLM's axial resolving power of 260-290 nm in solvent based media is the highest reported z-resolution in diffraction limited light-sheet microscopy (i.e. without requiring super-resolution mechanisms such as stimulated emission depletion, nonlinear structured illumination, or localization approaches). Owing to ctASLM's excellent optical sectioning and high-quality raw data, deconvolution is however not always needed (Supplementary Figure 9). In comparison to a conventional light-sheet microscope ( Figure  1c), individual synaptic spines could be clearly resolved in any spatial dimension ( Figure  1d), and neurons could be traced in 3D using unprocessed data.
Because of its large field of view and long working distance, we explored the ability of ctASLM equipped with the NA 0.4 objectives to image large tissues. Figure 1e shows a PEGASOS cleared, Thy1-GFP brain, (Supplementary Movie 2) and Figure 1f shows a volume rendering of a Neuron at a depth of 2.5 mm. Even at this depth, we were able to resolve fine neuronal structures and individual spines (Supplementary Movies 3 and 4, and Supplementary Figures 10 and 11). Using benzyl alcohol and benzyl benzoate clearing, we imaged a neonatal kidney labeled with the vasculature marker Flk1-GFP, and maintained sufficiently high spatial resolution to distinguish individual cells throughout the 3.4×2.6×2.5 mm 3 volume (Figure 1g, Supplementary Movie 5). We also imaged the hematopoietic stem cell (HSC) niche in a bone marrow plug (Figure 1h-k). HSCs are responsible for the continued production of blood and immune cells throughout life, and HSCs reside within a perisinusoidal niche in the bone marrow where Leptin Receptor+ (LepR+) stromal cells and endothelial cells synthesize the factors required for HSC maintenance. 19 However, careful analysis of the interactions between these cells has not been done, as HSCs are rare (0.003% of all bone marrow cells) and imaging large specimens has been prohibitively timeconsuming. 19 ctASLM imaging allowed us to identify all α-catulin+ c-kit+ HSCs, markers that identify all HSCs in young adult bone marrow and which give very high levels of purity ( Figure 1i). The isotropic resolution revealed extensive interactions of LepR+ cell projections and HSCs (Figure 1j-k), which were distorted when imaged with a confocal microscope (Supplementary Movie 6). Further we imaged the entire murine olfactory bulb (Supplementary Movie 7), which included ossified structures that would otherwise be incompatible with aqueous clearing methods and demonstrate the importance of being able to image in solvents that range in refractive index from 1.333 to 1.559.
Next, we tested whether the quality of ctASLM data is sufficient for the application of automated and unbiased approaches to image quantification. Using open source software, we could readily detect dendritic spines and cluster them based on their 3D morphology . Formerly, such an analysis was performed manually on hundreds of serialsectioned tissue preparations, as identification of individual cells in confocal datasets was particularly challenging due to its diminished z-resolution.
Lastly, we imaged HSCs, nerve fibers and vasculature in mouse bone marrow with the NA 0.7 objectives and 300 nm-scale resolution (Figure 3a-d, Supplementary Figure 14. Many c-kit+ HSCs were associated with fenestrations in the sinusoidal blood vessels, consistent with observations that HSCs migrate through these fenestrations to enter and exit circulation (Figure 3e-h). 19 Importantly, detailed features of the HSCs could not be resolved with a confocal microscope (Supplementary Figure 15), but were readily resolved by ctASLM due to its improved resolution. Intriguingly, we observed punctate staining along nerve fibers that resembled synapses, raising the possibility that nerve fibers directly interact with a subset of c-kit+ cells (Figure 3i-l, Supplementary Movie 11).
In summary, ctASLM simultaneously provides subcellular detail and tissue scale anatomy. As tissues are inherently 3D, the isotropic resolution of ctASLM is an important attribute to describe 3D morphologies and molecular concentrations in an unbiased way. Indeed, even higher resolutions can be achieved by combining ctASLM with Expansion Microscopy ( Supplementary Figures 16-18, and Supplementary Movies 12 and 13), albeit with one order of magnitude larger field of view than Lattice Light-Sheet Microscopy. Of note, ctASLM imposes a higher out-of-focus excitation load on the sample than a conventional light-sheet system and illuminates a smaller portion at a time, both of which can increase photobleaching. However, even at the highest resolution level (NA 0.7), we were not limited by photobleaching and were able to image a sample multiple times with high signal to noise ratio. Some steering of the light-sheet was observed deep in the PEGASOS brain and likely higher order aberrations are present in some of our CLARITY cleared samples (Supplementary Figure 19). Automated alignment routines, as well as adaptive optics could help to achieve diffraction limited performance in less than optimally cleared samples. With the given resolution level, we readily show that we can combine ctASLM with computer vision to detect and classify biological features throughout millimeters of tissue. Consequently, we believe that ctASLM will expedite human cell atlas efforts, providing much-needed insight into how tissue function manifests in health and disease, from the heterogenous cellular populations that compose it.

Animal Specimens
All animal protocols were approved by local Institutional Animal Care and Use Committees (IACUC) as directed by the National Instutes of Health, and strictly followed. These protocols include AC-AAAR0417 (to R.T., Columbia University), 2017-102370 (to J.M., UT Southwestern Medical Center), 101917 (to D.M., UT Southwestern Medical Center), 101715 (to I.B., UT Southwestern Medical Center), and 102632 (to S.M., UT Southwestern Medical Center).

Microscope Control
The data acquisition computer for ctASLM-v1 was a Dell Precision 5810 Tower equipped with an Intel Xeon E5-1650W v3 processor operating at 3.5 GHz with 6 cores and 12 threads, 128 GB of 2133 MHz DDR4 RAM, and an integrated Intel AHCI chipset controlling 4× 512 GB SSDs in a RAID0 configuration. The data acquisition computer for ctASLM v2 was a Colfax ProEdge SXT9800 Workstation equipped with two Intel Xeon Silver 4112 processors operating at 2.6 GHz with 8 cores and 16 threads, 96 GB of 2.667 GHz DDR4 RAM, a Intel DC P3100 1024 GB M.2 NVMe drive, and a Micron 5200 ECO 7680 GB hard-drive for file storage. All software was developed using a 64-bit version of LabView 2016 equipped with the LabView Run-Time Engine, Vision Development Module, Vision Run-Time Module and all appropriate device drivers, including NI-RIO Drivers (National Instruments). Software communicated with the camera (Flash 4.0, Hamamatsu) via the DCAM-API for the Active Silicon Firebird frame-grabber and delivered a series of deterministic TTL triggers with a field programmable gate array (PCIe 7852R, National Instruments). These triggers included control of the resonant mirror galvanometers, voice coils, stage positioning, laser modulation and blanking, camera fire and external trigger. Some of the core functions and routines in the microscope control software are licensed under a Material Transfer Agreement from Howard Hughes Medical Institute, Janelia Research Campus. The control software code for ctASLM can be requested from the corresponding authors and will be distributed under MTA with HHMI Janelia Research Campus.

ctASLM-v1 Microscope Layout
A schematic layout of the ctASLM-v1 microscope is shown in Supplementary Figure 20  , and directed through a 3× Galilean beam expander (GEB03-A). Thus, the initial beams were expanded by a factor of 12× before being focused onto a resonant mirror galvanometer (CRS 4 kHz, Cambridge Technology) with a cylindrical lens (ACY254-50-A, ThorLabs). The mirror galvanometer was used to wobble the light sheet and was driven with a 12V power supply (A12MT400, Acopian), and the 1D focus of the cylindrical lens was relayed with a 200 mm achromatic doublet (AC508-200-A, ThorLabs), passed through a 50:50 polarizing beam splitter and a quarter waveplate, and imaged onto a mirror with a 4× microscope objective (XL Fluor 4×/340, NA 0.28, Olympus Life Sciences). The mirror was mounted on a voice coil with 10 mm of travel, sub-50 nm positioning repeatability, and a sub-3 millisecond response time (LFA-2010, Equipment Solutions). The reflected light was recollected by the same 4× objective, the polarization state was rotated by the second passage through the quarter waveplate (AQWP3, Boldervision) reflected with the cube beam splitter (10FC16PB.7, Newport) and relayed to the illumination objective (Cleared Tissue Objective, NA 0.4 Advanced Scientific Imaging) with 200 mm and 75 mm achromatic doublets (AC508-200-A and AC508-75-A, ThorLabs). To correct for spherical aberrations, 3 mm N-BK7 glass piece (37-005, Edmund Optics) was placed between the 4× objective and the voice coil actuated mirror (Supplementary Figure 21).
To collect a Z-stack, a voice coil direct drive was used to scan the specimen in a samplescanning mode (V-522.1AA, C-413.2GA, and C-413.1IO, Physik Instrumente). Subvolumes were acquired by moving the position of the specimen and the voice coil stage with a 3-axis motorized stage (3DMS and MP-285A, Sutter Instruments). To image a 1mm 3 volume using ctASLM takes 17.6 minutes, using 100 ms exposure time, an isotropic step size of 0.425 μm and 18% overlap between stacks for faithful stitching.

ctASLM-v2 Microscope Layout
ctASLM-v2 was built essentially identical to ctASLM-v1, with a few minor differences noted here. A schematic drawing of the setup is provided in Supplementary Figure 22 and a parts list is provided in Supplementary Table 5. The illumination train consisted of three Coherent Obis lasers (LS 561-100C, LX 647-120C, and 488-150C) housed in an Obis LX/LS Laser Box that permits user-selectable input impedances and improved thermal management. All three lasers were combined with dichroic beamsplitters (LM01-503-25, LM01-613-25, Semrock), their polarization rotated with an achromatic half wave plate (10RP52-1B, Newport), focused with a 50 mm achromatic doublet (AC254-50-A, ThorLabs) through a 30 μmpinhole (P30D, ThorLabs), recollimated with a 100 mm achromatic doublet (AC254-100-A, ThorLabs), and expanded an additional 5x with a beam expander (BEO5M, ThorLabs). A cylindrical lens (ACY254-50-A, ThorLabs) was used to create a 1-dimensional laser line focus on a 4 kHz resonant galvo (6SC04KA040-01Y, Cambridge Technology), which was used to wobble the light sheet and was driven with a 12V power supply (A12MT400, Acopian), and the 1D focus of the cylindrical lens was relayed with a 100 mm achromatic doublet (AC254-100-A, ThorLabs), passed through a polarizing polarizing beam splitter (10FC16PB.7, Newport), an achromatic quarter wave plate (10RP54-1B, Newport), and imaged onto a mirror with a 40x microscope objective (CFI S Plan Fluor ELWD 40XC, NA 0.60, Nikon Instruments). The mirror was mounted on a voice coil with 10 mm of travel, sub-50 nm positioning repeatability, and a sub-3 millisecond response time (LFA-2010, Equipment Solutions). The reflected light was recollected by the same 40× objective, the polarization state was rotated by the second passage through the quarter waveplate, reflected with the polarizing beam splitter and relayed to the illumination objective (Cleared Tissue Objective, NA 0.7 Advanced Scientific Imaging) with 80 mm and 200 mm achromatic doublets (AC508-200-A and AC508-75-A, ThorLabs).

Sample mounting
Samples were glued with cyanoacrylate (PT09, Pacer Technology, California) or with silicone (GE Silicone 2+) on to a 1×2.5 cm 2 glass slide cut from a 2.5×7.5 cm 2 microscope slide (Cat. No. 3050, Thermo Scientific). The glass slides were mounted with a custom holder to the sample scanning stage. CAD drawings of the sample holder assembly are shown in Supplementary Figure 23.

Conventional Light-Sheet Microscopy
For conventional low-NA light-sheet imaging, the same optical train was used as in ctASLM-v1, but with the presence of a variable width slit aperture conjugate to the back pupil of the cylindrical lens, and without axial scanning of the illumination beam. The illumination NA was selected such that the entire field of view (870 × 870 μm 2 ) resided within two Rayleigh lengths of the illumination beam. This resulted in a Gaussian beam with a e −2 thickness of ~7 μm, which is in agreement with resolution measurements obtained using sub-diffraction fluorescence beads. With the ctASLM-v2 microscope (NA 0.7 objectives), an axial resolution of 2.5 microns resulted, which we believe is in large part due to the close to diffraction limited detection PSF.

Confocal Microscopy
Imaging of the bone plug was performed on a Zeiss LSM880 equipped with a long workingdistance multi-immersion objective (LD LCI Plan-Apo 25× 0.8 NA) as previously described. 21,22 PEGASOS cleared mouse brain were also imaged using a Leica SP8 confocal laser scanning microscope equipped with an HCX APO L20x/0.95 immersion objective. For best performance a 56 μm pinhole size with a pixel dwell time of 1.84 μs was used. A comparison of ctASLM to the data acquired with the Leica SP8 is shown in Supplementary  Figure 24.

Sample Preparation
Zebrafish-The zebrafish embryo is a transgenic line (krdl:mCherry) expressing mCherry on the vascular endothelium. A 3-day post fertilization zebrafish embryo is mounted in 2% low-melting agarose (A9045-25G, Sigma-Aldrich) in an FEP (Fluorinated ethylene propylene) tube (FEP HS .029 EXP/ .018 REC, Zeus). The working solvent of the agarose is E3 fish water. The FEP tube is sonicated in 70% Ethanol and then washed with clean distilled water prior to the mounting of the zebrafish embryo. We first pick and leave a zebrafish embryo in a few (~50 μL) E3 media on a petri dish. Next, 5 μL of 0.1% Tricaine is added to anesthetize the zebrafish embryo. About 50 μL of the mixture of the Tricaine and E3 medium is removed, and then the liquid low-melting agarose is added on the zebrafish embryo. The zebrafish embryo in the liquid agarose is aspirated directly into the clean FEP tube with a pipette. Within a few minutes, the agarose is solidified, and the zebrafish embryo is mounted in the FEP tube. For the last step, we use a needle to poke two holes on the FEP tube above and below the zebrafish embryo to increase the gaseous exchange allowing the embryo to breathe. The zebrafish embryo is then transferred into the microscope chamber and ready to be imaged. CLARITY Brain-Thy1-eYFP brain were extracted and cleared using passive CLARITY 23 with the following hydrogel monomer (HM) solution recipe: 1% (wt/vol) acrylamide, 0.05% (wt/vol) bisacrylamide, 4% paraformaldehyde, 1x Phosphate Buffered Saline, deionized water, and 0.25% thermal initiation VA-044 (Wako Chemicals, NC0632395). The transcardiac perfusion was performed with 20 ml HM solution, followed by brain extraction and overnight incubation at 4°C. The Hydrogel was polymerized by incubating the brain samples at 37°C for 3-4 hours, and the clearing was performed by shaking in clearing buffer (4% (wt/vol) SDS, 0.2 M boric acid, pH 8.5 at 37°C, ~3 weeks). After washing with the buffered solution (0.2 M boric acid buffer, pH 7.5, 0.1% Triton X-100), the brains were transferred into 50-87% glycerol solution for refractive index matching (as described previously 24 ) and imaging.
Olfactory Bulb-Double transgenic mice were made by crossing Ai9 mice (B6.Cg-Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J, Jax Laboratory Stock No. 007909) with FosTRAP mice 25 and resulting mice were termed cFosxAi9 mice. Briefly, animals were anesthetized with IP injections of ketamine/xylazine (120mg/kg ketamine and 16mg/kg xylazine). After verification of complete anesthesia via the toe pinch test, the chest cavity was exposed and a syringe needle was inserted into the left ventricle. Mice were perfused with 20mL of PBS solution followed by 20mL of a 4% PFA Solution. After perfusion, mice were decapitated and the brain, including the olfactory bulbs, were carefully dissected out and placed into 4% PFA solution overnight. These samples were then cleared using the PEGASOS method as described in Jing et al. 26 All samples were kept at 42°C for this process. Briefly, samples were decolorized for 2 days in 25% Quadrol before undergoing delipidation in a series of 30%, 50%, and 75% tert-Butanol solutions. This was followed by a final delipidation step with TBP (75% tert-butanol and 25% PEGMMA solution). Finally, samples were submerged in BB-PEG (75% benzyl benzoate, 22% PEGMMA, 3% Quadrol) until imaging. These data shown here were based upon a male mouse aged 3.32 months.
Bone Marrow-For imaging of blood vessels or niche cells, double or triple transgenic mice were generated by crossing B6.Cg-Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J (Jax#007909) mice to Cdh5-creER (B6.CBA-Tg(Cdh5-cre/ERT2)1Rha) or Leptin receptorcre and alpha-catulin-GFP mice. 21,27 To induce Cre recombinase activity, Cdh5-creER;Rosa26-tdTomato mice received an intraperitoneal injection of 2 mg of tamoxifen dissolved in corn oil every second day for 10 days (5 injections total). For imaging of blood vessels, bones were embedded into OCT embedding medium. 21 Using a cryostat, femurs were then cut in half lengthwise and then re-mounted at a 90 degree angle to the original cut, and cut in half lengthwise again, resulting in a quarter bone. For all other samples, intact bone marrow plugs from freshly dissected tibias of 6−8-week-old mice were extruded from the bone using a PFA-filled syringe with a 25-gauge needle and placed directly into 4% PFA solution for 6 h at room temperature. Processing of samples were carried out as previously described in Acar et al. 21 Briefly, fixed quarter bones or plugs were then washed in PBS and blocked in whole mount staining medium (PBS with 5% donkey serum, 0.5% NP-40, and 10% DMSO) overnight at room temperature. After blocking, plugs were stained for three days at room temperature with primary antibodies (chicken anti-GFP: GFP-1020, Aves Labs and goat anti-c-kit: BAF1356, R&D Systems) or (rabbit anti tyrosine hydroxylase at 1-250 (EMD Millipore cat#AB152), rabbit anti peripherin at 1-250 (EMD Millipore cat#AB1530), and goat anti cKit at 1-250 (R&D Systems cat# BAF1356)) in staining solution. Then the samples were washed multiple times in PBS at room temperature for one day and put into staining solution containing secondary antibodies (Alexa Fluor 488-AffiniPure F(ab')2 fragment donkey anti-chicken IgY and Alexa Fluor 647-AffiniPure F(ab')2 fragment donkey anti-goat IgG) or (Alexa Fluor 488-AffiniPure F(ab')2 fragment donkey anti-rabbit IgG, Alexa Fluor 647-AffiniPure F(ab')2 fragment donkey anti-goat IgG) for three days followed by a one-day wash to remove any unbound secondary antibodies. Nerve stained marrow plugs were embedded in 2% low melt agarose in water and were dehydrated in a series methanol washes, followed by refractive index matching for 1 hour at room temperature in 1:2 benzyl alcohol (BA): benzyl benzoate (BB). Quarter bones and bone marrow niche plugs embedded in 2% low melt agarose were prepared using the PEGASOS method. 26 Briefly, samples were dehydrated in a series of 30%, 50%, 70%, and 100% Tert-Butanol. Each step was performed for 8 hours at room temperature. Samples were then refractive index matched overnight at room temperature in BB-PEG. BB-PEG was prepared as a 3:1 mixture of benzyl benzoate (BB) (Sigma-Aldrich B6630) and PEGMMA500 (Sigma-Aldrich 447943) supplemented with 3% w/v Quadrol (Sigma-Aldrich 122262). Samples were imaged 1 day to 2 months after initial refractive index matching without significant loss of endogenous TdTomato or Alexa Fluor signal.
Isolated Hippocampus-Thy1-GFP mouse was transcardially perfused with cold PBS first, then 4% PFA in PBS for fixation. The brain was taken out and put in 4% PFA in PBS overnight at 4°C, then 2 mm thickness hippocampal slices was cut with vibratome coronally. The hippocampal slices were then cleared with PEGASOS clearing protocol, previously described in Jing et al. 26 Briefly, the slices were decolorized in 25% Quadrol for 1 day, then delipidated in series of 30%, 50%, 70% Tert-Butanol solutions, dehydrated in tB-PEG, and finally cleared in BB-PEG.
Expanded Hippocampus-For imaging expanded hippocampal samples from either Thy1-GFP mice ( Supplementary Figures 16 and 18) or WT mice injected with Synaptotag AAV (Supplementary Figure 17) were used. 28 The mice were transcardially perfused with cold PBS first, then 4% PFA in PBS for fixation. The brain was taken out and put in 4% PFA in PBS overnight at 4°C, then 0.1 mm thickness coronal hippocampal slices were cut with vibratome. Samples were processed according to the original protein-expansion microscopy procedure. 17,29 Briefly, slices were permeabilized using 5% BSA, 0.5% Triton X-100 in PBS for 1 h at room temperature and stained with rat anti-mCherry (Thermo Scientific M11217, 1:500) (Supplementary Figure 17 Figure 18) conjugated antibodies at room temperature for 4 h. After 3 washing steps samples were incubated overnight with a coupling reagent 0.1% acryoyl-X succinimidyl ester (AcX) in PBS. 29 For results shown on Supplementary Figure 16 the samples were directly crosslinked with AcX reagent without further antibody staining against GFP. Slices were washed 3 times with PBS for 5 minutes and incubated in polymerization solution (8.6% sodium acrylate, 2.5%, 0.15% N,N -Methylenebisacrylamide, 2 M NaCl, 0.2% APS, 0.2% TEMED, 0.01% TEMPO) for 1 h at 4 C before prior to polymerization at 37 C for 2 h. Embedded tissue was transferred to isotonic proteinase K buffer (50 mM Tris-HCl pH 8.0, 2 M NaCl, 1 mM EDTA, 0.5% Triton X-100, proK 8 u/mL) and incubated for 2 h at room temperature. Complete proK digestion was achieved overnight at room temperature in proteinase K buffer (50 mM Tris-HCl pH 8.0, 1 M NaCl, 1 mM EDTA, 0.5% Triton X-100, proK 8 u/mL). Next day, samples were expanded for 1 h in double-distilled H 2 O with 3 water changes. Glass holders were covered with 0.1% poly-L-lysine for 1 h, washed 3 times with ddH 2 O and allowed to air dry for 1 h. Hippocampus region was excised from coronary slices and mounted on a poly-L-lysine coated glass holder using wax as additional support on the sides. For long-term storage and image acquisition samples were transferred to 1 mM NaOH buffered solution.
Neonatal Kidney Preparation-Post-natal day 3 kidneys from an Flk1-GFP (Jackson Laboratories, 017006) mouse were harvested and fixed for 2h at room temperature in 4% paraformaldehyde in PBS. Procedures were performed according to UTSW-IACUCapproved guidelines. Fixed kidneys were incubated with tert-Butanol/water mixtures of increasing alcohol concentrations (30%, 50%, 70%, 80%, 96%, and twice 100%) with 2% Triethanolamine. The kidneys were then left in BABB (benzyl alcohol and benzyl benzoate in a 1: 2 volume ratio) with 10% Triethanolamine until tissue cleared. 6 For prolonged storage in clearing medium (> 2 days), samples were stored at 4⁰C. Given that the tissue was harvested from neonatal mice, and no genotyping was peformed, the gender of the mouse is unknown.

Stitching and 3D visualization
Image processing was performed on a Windows 10-based workstation equipped with two Intel Xeon Gold 5120 CPUs, 1 TB of RAM, an NVIDIA Quadro P6000 GPU. To stitch the sub-volumes, the Fiji-based plugin BigStitcher 30 was used. Image analysis was performed with Fiji 31 and MATLAB (Mathworks), and 3D renderings were produced with ChimeraX or Arivis. Table 6 list details for each data set shown in this manuscript). When using Richardson-Lucy algorithm to deconvolve the images, the point spread function was extracted from the image of a single bead, centered and background subtracted and 3D-deconvolution was performed with 20 iterations of the Richardson-Lucy algorithm (Microvolution). For the Blind deconvolution, we use deconvblind function in MATLAB (Mathworks) and the following procedure. Prior to the deconvolution, we rotationally average along the optical axis the experimental image of a single 200 nm bead. We measure the mean value of the background in the rotationally average PSF and subtract it from the PSF image. In order to improve this experimental PSF, we first deconvolve the raw image with the rotationally averaged and background-subtracted PSF for 5 iterations. This yields a restored PSF from the devonvblind routine (i.e. the resulting PSF will more closely approximate the real PSF in the measurement). In a second step we then use this restored PSF to perform blind deconvolution on the raw image data, which then results in the final deconvolved image. For biological data we used 5-10 iterations, whereas for the bead analysis, we limited the number of iterations to 5. The raw images and the experimental PSF were resampled 2x with bicubic algorithm with ImageJ before the deconvolution, which was particularly necessary for the ctASLM 1 data (NA 0.4), which has not much Nyquist oversampling in its data acquistion. ctASLM2 was designed with more oversampling (larger optical magnification), hence computational resampling before deconvolution is not necessary.

Data analysis
HSC Detection-HSCs were detected as previously described. 21 Briefly, individual alphacatulin-GFP+, Kit+ HSCs were identified manually using the orthoslicer function of Bitplane Imaris v9.2.1 software. HSC coordinates and size were interactively annotated using the Imaris spots function in manual mode. Kit and LepR TdTomato surfaces were generated in Imaris using iso-surface function based on an empirically chosen intensity threshold.
Glomeruli Detection-In an effort to automatically detect the abundance of glomeruli throughout the entire neonatal kidney tissue, sub-volumes (1024 × 1024 × 470 pixels) were retrieved from the stitched volume (3042 × 4018 × 2757 pixels) and loaded into MATLAB. Sub-volumes were subjected to a three-dimensional Gaussian blur of 20 pixels before individual glomeruli were detected using an auto-adaptive watershed algorithm implemented in MATLAB. 32 This algorithm uses a difference of Gaussians to suppress features that are either too large or small, here defined as 100 and 10 pixels, respectively. Segmented objects touching the border of the sub-volume were removed. After identification of glomeruli, the encompassing region was again loaded into MATLAB, and the full resolution was subjected to the same watershed algorithm, but with a difference of Gaussian of 10 and 2 pixels, respectively.
Spine Detection and Clustering-We detected spines using previously described software. 20 Images were first deconvolved for 10 iterations using the MATLAB function deconvblind(), which uses a maximum likelihood algorithm to infer the fluorescence distribution in the sample. Following a gamma transform of 0.4 and 3D hole filling, a triangle mesh representing the cell surface was created as an isosurface at the Otsu threshold value of the image. The mesh was next decomposed into approximately convex patches using the parameters previously described, 20 except that merging via the triangle criterion was disabled and merging via the line-of-sight criterion was performed more conservatively with a parameter of 0.8. Training data for patch merging and patch classification machinelearning models were then generated from nine non-overlapping images of size 200 pixels in the lateral dimensions and approximately 150 pixels in the axial dimension. These nine images were cropped from eight larger images. Using leave-one-out cross validation on patches labeled as certainly spines or certainly not spines, we calculated that our spine detection workflow had a precision of 0.86 and a recall of 0.88.
We performed an unsupervised clustering of detected spines into 15 clusters using agglomerative hierarchical clustering. For each cluster, we calculated the median of each of the morphological feature vectors used for clustering and plotted the spine closest to that median (Figure 2b). Only the clusters with a median spine attached to the neuron body were plotted. Principal component analysis was performed on these same features. We defined the spine neck surface area, as the patch closure surface area. We previously defined 20 the closure surface area as the additional surface area needed to minimally generate a closed polyhedron from the open polyhedron comprising the dendritic spine. We also defined the variation from a sphere by fitting each spine to a sphere and measuring the ratio of the standard deviation of distances from the sphere at each mesh face to the sphere radius.
Segmentation of Neurons-The mouse brain volume was tiled in sub-volumes for parallel processing, and the signal was enhanced using a volumetric Laplacian-of-Gaussian transform with an isotropic scale of 1.5 voxels. The resulting volume was then transformed into a triangular mesh using an empirically chosen threshold. In order to reduce clutter, only the connected components with volume superior to the 99 percentiles of all connected component volume are displayed in Figure 1f. The algorithm is implemented using the opensource G'MIC software (https://gmic.eu), and the mesh was imported into Meshlab 33 for coloring and image capture.
Statistics and Reproducibility-For statistical analysis, we report the mean, standard deviation, and number of observations. For PSF measurements of the different microscopes and immersion media, the number of measurements ranged from 5-10 for light-sheet microscopy and three for airy scan microscopy. The values are listed in Table 1 in the online methods.
Repeatability: Generally, only one sample was used for each imaging experiment (e.g. imaging of whole mouse brain, kidney, etc.), unless otherwise noted. Two expanded brain slices were prepared and imaged with either the NA 0.4 or NA 0.7 version of ctASLM. The number of times that each imaging operation was repeated for each figure is indicated in Supplementary Table 6 and amounted to one in most cases. Segmentation of neurons was perfomed on one data set from a CLARITY brain sub-volume and on one data set from a hippocampal slice sub-volume that was cleared with PEGASOS. Synaptic spines were detected on two different sub-volumes from a CLARITY brain.

Supplementary Material
Refer to Web version on PubMed Central for supplementary material.    (j) showing an interaction between a nerve fiber and a c-kit+ cell. (l) Volume rendering of (j). Data acquired with NA 0.7 objectives. Scale bar: (a) 500 μm (b&c) 10 μm (d) 2.5 μm (e&f) 5 μm (i) 10 μm (j&k) 2.5 μm Table 1 Experimental values for point spread functions. Note that the magnification and resolving power slightly changes for the multi-immersion lenses used for ctASLM for refractive indices (η) between 1.333 and 1.56, respectively. All numbers presented are the mean ± the standard deviation of the Full-Width Half-Maximum, and the sample size is 5 for each measurement. ---not measured.


extfieldsofstudy: Medicine Chemistry

text:
Isolation of cellulase enzyme from brown garden snail (Cornu aspersum) for the saccharification of waste paper materials

Graphical abstract

Specifications
Environmental Science More specific subject area: Biotechnology and renewable bioresources Method name: Isolation of Cornu aspersum (brown garden snail) cellulase enzyme for saccharification of the cellulose component of waste paper materials. Name and reference of original method: Designed and validated in our laboratories.

Method details
Lignocellulosic biomass is one of the most abundant, low cost and renewable sustainable feedstock available for the production of bio-chemicals due to the rich cellulose content of this biopolymer. Cellulose is a polymerized form of glucose molecules with β-1,4-linkages consisting of composite forms of highly crystalized microfibrils among amorphous matrixes, thus refusing access to hydrolyzing enzymes [1]. Much of the cellulosic waste is often disposed of by biomass burning which is a process that is not environmentally friendly. Cellulolysis can be applied to convert cellulose to glucose which is a multi-utility product for bioenergy and polyesters. The conversion of cellulose to glucose can be done in a much cheaper and biologically favourable process. The best cellulase systems used in cellulolysis are from the microbial system found in the gut of organisms living on cellulosic biomass as their major energy source, for example garden snails [2].

Preparation of solutions
An amount of 0.6 g of trisÀHCl buffer (Merck) was weighed and transferred into a 1000 ml volumetric flask and filled with distilled water to prepare a solution with concentration of 0.005 M. The pH of this solution was adjusted to pH 5 using a 32% hydrochloric acid (HCl), (Minema) and a 0.5 M potassium hydroxide (KOH), (Rochelle chemicals). Methanol (10 ml, Scienceworld) was added to the trisÀHCl buffer solution to prevent the growth of micro-organisms and stored at 4 C. The trisÀHCl buffer (pH 5) solution was used during all incubation procedures when the various paper materials (filter paper, office paper, Woolworths paper, newspaper, foolscap paper, Pick n Pay paper, kraft or brown envelope paper) were saccharified with the snail cellulase.
Glucose standard solutions with concentrations of 0.5 mg ml À1 , 2 mg ml À1 , 4 mg ml À1 , 6 mg ml À1 and 8 mg ml À1 were used to construct a standard curve that was used to conclude the concentration of the sugars released by the cellulase action. Protein (albumin) standard solutions of concentrations 5 mg ml À1 , 10 mg ml À1 , 15 mg ml À1 , 20 mg ml À1 and 25 mg ml À1 were to construct a protein standard curve that was used to calculation the amount of protein from the dialysed snail cellulase enzyme.

Garden snail cellulase extraction
Garden snails, represented in Fig. 5 were collected from the garden and sacrificed by drowning them in water for a period of 24 h as represented in Fig. 6. The sacrification of the garden snails was performed without the use of any chemical to prevent chemical interference with the cellulase action on waste paper materials. The shell of the snails as well as the feet were removed. Fig. 8 represents the foot and the visceral organs of the snail. The weighed visceral organs were cut into very small pieces ( Fig. 9) and homogenised in trisÀHCl buffer with a blender and thereafter stirred for a period of 1 h. The homogenate, represented in figure 10 was transferred into a test tube and centrifuged using Beckman, GP Centrifuge (UK, Marca) 3750RPM 1-98-345369 (figure 7) for 30 min and the collected supernatant transferred into a pre-soaked dialysis tube. A dialysis tube (30 cm Â 3 cm) was soaked in water at 4 C for 4 h before it was filled with the supernatant. The dialysis tube filled with the supernatant was dialysed in distilled water at 4 C for 18 h where after the protein content of the dialysed solution was determined by the Buiret test method [3]. The dialysed extract of the cellulase enzyme was used to biodegrade the cellulose component of waste paper materials (filter paper, office paper, Woolworths paper, newspaper, foolscap paper, Pick n Pay paper, kraft or brown envelope paper) into fermentable sugars such as glucose.

Waste paper saccharification with Cornu aspersum cellulase
Rapid population increase, urbanization and industrialization have resulted in the generation of huge amounts of many types of wastes which necessitate proper management [4][5][6][7][8]. The paper materials were prepared as round discs (using a puncher) with a diameter of 6 mm and 20 pieces of each paper material (filter paper, office paper, Woolworths paper, newspaper, foolscap paper, Pick n Pay paper, kraft or brown envelope paper) was weighed and the mass recorded as represented in Table 1 below.
The weighed pieces of different paper materials were transferred into labelled test tubes filled with 800 ml of trisÀHCl buffer (pH 5) and 200 ml of dialysed snail cellulase enzyme. The cellulase-waste paper incubation was performed in triplicates at 50 C for 2 h. After incubation the tubes were centrifuged for 15 min using Thermo Scientific SL8 centrifuge (Germany), Rotor 75005701 and the supernatants were transferred into clean test tubes with the concentration of the produced sugars determined by the DNS method [9]. The precipitate of papers collected after centrifugation was kept at 4 C for the next incubation period with fresh dialysed snail cellulase enzyme. TrisÀHCl buffer (pH 5) of 800 ml and fresh dialysed snail cellulase of 200 ml were mixed with the precipitated paper material from the previous incubation, vortexed thoroughly and incubated at 50 C for another 2 h. Re-incubation of precipitated papers was repeated for 10 consecutive incubation periods with the amount of sugars produced during each incubation period determined by the DNS method. All absorbance readings were taken using the Shimadzu UV Vis 1800 (Kyoto, Japan) spectrophotometer. For the concentration determination, the glucose standards and dialysed snail enzyme were read at 520 nm wavelength whereas the protein standards were read at 545 nm wavelength.

Results and discussion
The amount of sugars present in the dialysed snail cellulase enzyme as well as the trisÀHCl buffer used as a control, was calculated using the standard curve (Fig. 1) to be 0.23 mg ml À1 and 0.67 mg ml À1 respectively. This was evidence that during the dialysis method, sugars were removed from the snail material.
The amount of concentration of the dialysed snail enzyme and trisÀHCl buffer is represented in Fig. 2 below. The amount of protein present in the dialysed snail enzyme was calculated using the standard curve (Fig. 3) to be 13.37 mg ml À1 . This is the amount of protein retained in the snail material during the dialysis method.
The total amount of sugar released from each paper material during the various successive incubation periods is illustrated in Fig. 4. These results indicate that the highest yield of sugar was produced from foolscap paper at a total concentration of 37 mg ml À1 and at a relative percentage saccharification of 65%. Kraft (brown envelope) paper released the second highest total amount of sugars (35 mg ml À1 ) and a percentage saccharification 65%. The total amount of sugar released from filter paper, office paper and Woolworths advertising paper were the same at 21 mg ml À1 and thus produced the third highest sugar concentration. The percentage saccharification of these three paper materials are however not identical as filter paper resulted in 68% saccharification followed by office paper at 55% and Woolworths paper at 50%.

Conclusion
Although the bioconversion of waste cellulose with garden snail (Cornu aspersum) cellulase produced fermentable sugars such as glucose, the process needs more investigation to make it commercially viable. The current investigation is at an initial stage of the development process and the cost effectiveness of the procedure needs to be analysed. Future prospect of the research will include: