extfieldsofstudy:
Biology
Computer Science

s2fieldsofstudy:
Biology
Computer Science

text:
Cell “hashing” with barcoded antibodies enables multiplexing and doublet detection for single cell genomics

Despite rapid developments in single cell sequencing technology, sample-specific batch effects, detection of cell doublets, and the cost of generating massive datasets remain outstanding challenges. Here, we introduce cell “hashing”, where oligo-tagged antibodies against ubiquitously expressed surface proteins are used to uniquely label cells from distinct samples, which can be subsequently pooled. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its sample of origin, and robustly identify doublets originating from multiple samples. We demonstrate our approach by pooling eight human PBMC samples on a single run of the 10x Chromium system, substantially reducing our per-cell costs for library generation. Cell “hashing” is inspired by, and complementary to, elegant multiplexing strategies based on genetic variation, which we also leverage to validate our results. We therefore envision that our approach will help to generalize the benefits of single cell multiplexing to diverse samples and experimental designs.

INTRODUCTION
Single cell genomics offers enormous promise to transform our understanding of heterogeneous processes and to reconstruct unsupervised taxonomies of cell types 1,2 . As studies have progressed to profiling complex human tissues 3,4 and even entire organisms 5,6 , there is a growing appreciation of the need for massively parallel technologies and datasets to uncover rare and subtle cell states [7][8][9] . While the per-cell cost of library prep has dropped, routine profiling of tens to hundreds of thousands of cells remains costly both for individual labs, and for consortia such as the Human Cell Atlas 10 .
Broadly related challenges also remain, including the robust identification of artifactual signals arising from cell doublets or technology-dependent batch effects 11 . In particular, reliably identifying expression profiles corresponding to more than one cell (hereby referred to as 'multiplets') remains an unsolved challenged in single cell RNA-seq (scRNA-seq) analysis, and a robust solution would simultaneously improve data quality and enable increased experimental throughput. While multiplets are expected to generate higher complexity libraries compared to singlets, the strength of this signal is not sufficient for unambiguous identification 11 . Similarly, technical and 'batch' effects have been demonstrated to mask biological signal in the integrated analysis of scRNA-seq experiments 12 , necessitating experimental solutions to mitigate these challenges.
Recent developments have poignantly demonstrated how sample multiplexing can simultaneously overcome multiple challenges 13,14 . For example, the demuxlet 13 algorithm enables the pooling of samples with distinct genotypes together into a single scRNA-seq experiment. Here, the sample-specific genetic polymorphisms serve as a fingerprint for the sample of origin, and therefore can be used to assign each cell to an individual after sequencing. This workflow also enables the detection of multiplets originating from two individuals, reducing nonidentifiable multiplets at a rate that is directly proportional to the number of multiplexed samples. While this elegant approach requires pooled samples to originate from previously genotyped individuals, in principle any approach assigning sample fingerprints that can be measured alongside scRNA-seq would enable a similar strategy. For instance, sample multiplexing is frequently utilized in flow and mass cytometry by labeling distinct samples with antibodies to the same ubiquitously expressed surface protein, but conjugated to different fluorophores or isotopes, respectively 15,16 .
We recently introduced CITE-seq 17 , where oligonucleotide-tagged antibodies are used to convert the detection of cell-surface proteins into a sequenceable read-out alongside scRNA-seq. We reasoned that a defined set of oligo-tagged antibodies against ubiquitous surface proteins could uniquely label different experimental samples. This enables us to pool these together, and use the barcoded antibody signal as a fingerprint for reliable demultiplexing. We refer to this approach as cell "hashing", as our set of oligos defines a "look up table" to assign each multiplexed cell to its original sample. We demonstrate this approach by labeling and pooling eight human PBMC samples, and running them simultaneously in a single droplet based scRNA-seq run. Cell hashtags allow for robust sample multiplexing, confident multiplet identification, and the discrimination of low-quality cells from ambient RNA. In addition to enabling 'super-loading' of commercial scRNA-seq platforms to substantially reduce costs, this strategy represents a generalizable approach for doublet identification and multiplexing that can be tailored to any biological sample or experimental design.

Hashtag-enabled demultiplexing based on ubiquitous surface protein expression
We sought to extend antibody-based multiplexing strategies 15,16 to scRNA-seq using a modification of our CITE-seq method. We chose a set of monoclonal antibodies directed against ubiquitously and highly expressed immune surface markers (CD45, CD98, CD44, and CD11a), combined these antibodies into eight identical pools (pool A through H), and subsequently conjugated each pool to a distinct hashtag oligonucleotide (henceforth referred to as HTO, Figure  1A; Methods). The HTOs contain a unique 12-bp barcode that can be sequenced alongside the cellular transcriptome, with only minor modifications to standard scRNA-seq protocols. We utilized an improved and simplified conjugation chemistry compared to our previous approach using iEDDA click chemistry to covalently attach oligonucleotides to antibodies 18 (Methods).
We designed our strategy to enable CITE-seq and cell "hashing" to be performed simultaneously, but to generate separate sequencing libraries. Specifically, the HTOs contain a different amplification handle than our standard CITE-seq antibody derived tags (ADT) (Methods). This allows HTOs, ADTs, and scRNA-seq libraries to be independently amplified and pooled at desired quantities. Notably, we have previously observed robust recovery of antibody signals from highly expressed epitopes due to their extremely high copy number. This is in contrast to the extensive 'drop-out' levels observed for scRNA-seq data, and suggests that we can faithfully recover HTOs from each single cell, enabling assignment to sample of origin with high fidelity.
To benchmark our strategy and demonstrate its utility, we obtained PBMCs from eight separate human donors (referred to as donors A through H), and independently stained each sample with one of our HTO-conjugated antibody pools, while simultaneously performing a titration experiment with a pool of seven immunophenotypic markers (Methods) for CITE-seq. We subsequently pooled all cells together in equal proportion, alongside an equal number of unstained HEK-293T cells (and 3% mouse NIH-3T3 cells) as negative controls, and ran the pool in a single lane on the 10x Genomics Chromium Single Cell 3' v2 system. Following the approach in Kang et al 13 , we 'super-loaded' the 10x Genomics instrument, loading cells at a significantly higher concentration with an expected yield of 20,000 single cells and 5,000 multiplets. Based on Poisson statistics, 4,365 multiplets should represent cell combinations from distinct samples and can potentially be discarded, leading to an unresolved multiplet rate of 3.1% Notably, achieving a similar multiplet rate without multiplexing would yield ~4,000 singlets. As the cost of commercial droplet-based systems is fixed per run, multiplexing therefore allows for the profiling of ~400% more cells for the same cost.
We performed partitioning and reverse transcription according standard protocols, utilizing only a slightly modified downstream amplification strategy (Methods) to generate transcriptome, HTO, and ADT libraries. We pooled and sequenced these on an Illumina HiSeq2500 (two rapid run flowcells), aiming for a 90%:5%:5% contribution of the three libraries in the sequencing data. Additionally, we performed genotyping of all eight PBMC samples and HEK-293T cells with the Illumina Infinium CoreExome array, allowing us to utilize both HTOs and sample genotypes (assessed by demuxlet 13 ) as independent demultiplexing approaches.
When examining pairwise expression of two HTO counts, we observed relationships akin to 'species-mixing' plots ( Figure 1B), suggesting mutual exclusivity of HTO signal between singlets. Extending beyond pairwise analysis, we developed a straightforward statistical model to classify each barcode as 'positive' or 'negative' for each HTO (Methods). Briefly, we modeled the 'background' signal for each HTO independently as a negative binomial distribution, estimating background cells based on the results of an initial k-medoids clustering of all HTO reads (Methods). Barcodes with HTO signals above the 99% quantile for this distribution were labeled as 'positive', and barcodes that were 'positive' for more than one HTO were labeled as multiplets. We classified all barcodes where we detected at least 200 RNA UMI, regardless of HTO signal.
Our classifications (visualized as a heatmap in Figure 1C) suggested clear identification of eight singlet populations, as well as multiplet groups. We also identified barcodes with negligible background signal for any of the HTOs (labeled as 'negatives'), consisting primarily (87.5%) of HEK and mouse cells. We removed all HEK and mouse cells from downstream analyses (Methods), with the remaining barcodes representing 13,964 singlets, and 2,463 identifiable multiplets, in line with expectations. Our classifications were also fully concordant with a tSNE embedding, calculated using only the eight HTO signals, which enabled the clear visualization not only of the 8 groups of singlets (donors A through H), but also the 28 small groups representing all possible doublet combinations ( Figure 1D). Moreover, we observed a clear positive shift in the distribution of RNA UMI/barcode for multiplets, as expected ( Figure 1E), while the remaining negative barcodes expressed fewer UMIs and may represent failed reactions or 'empty' droplets containing only ambient RNA. These results strongly suggest that HTOs successfully assigned each barcode into its original sample, and enabled robust detection of cross-sample multiplets. Performing transcriptomic clustering of the classified singlets enabled clear detection of nine hematopoietic subpopulations, which were interspersed across all eight donors ( Figure 1F).

Genotype-based demultiplexing validates cell "hashing"
We next compared our HTO-based classifications to those obtained by demuxlet 13 . Overall we observed strong concordance between the techniques, even when considering the precise sample mixture in called doublets ( Figure 2A). Exploring areas of disagreement, we identified 1,138 barcodes that were classified based on HTO levels as singlets, but were identified as 'ambiguous' by demuxlet. Notably, the strength of HTO classification for these discordant barcodes (represented by the number of reads assigned to the most highly expressed HTO) was identical to barcodes that were classified as singlets by both approaches ( Figure 2B). However, discordant barcodes did have reduced RNA UMI counts ( Figure 2C). We conclude that these barcodes likely could not be genetically classified at our shallow sequencing depth, which is below the recommended depth for using demuxlet, but likely represent true single cells based on our HTO classifications.
In addition, we also observed 2,547 barcodes that received discordant singlet/doublet classifications between the two techniques ( Figure 2D). We note that this does reflect a minority of barcodes (compared to 12,676 concordant classifications), and that in these discordant cases it is difficult to be certain which of these methods is correct. However, when we examined the UMI distributions of each classification group, we observed that only barcodes classified as doublets by both techniques exhibited a positive shift in transcriptomic complexity ( Figure 2D). This suggests that these discordant calls are largely made up of true singlets, but represent conservative false positives from both methods, perhaps due to ambient RNA or HTO signal. Consistent with this interpretation, when we restricted our analysis to cases where demuxlet called barcodes as doublets with >95% probability, we observed a 71% drop in the number of discordant calls ( Figure 2E).

Cell hashing enables the efficient optimization of CITE-seq antibody panels
Our multiplexing strategy not only enables pooling across donors, but also the simultaneous profiling of multiple experimental conditions. This is widely applicable for simultaneous profiling of diverse environmental and genetic perturbations, but we reasoned that we could also efficiently optimize experimental workflows, such as the titration of antibody concentrations for CITE-seq experiments. In flow cytometry, antibodies are typically run individually over a large dilution series to assess signal to noise ratios and identify optimal concentrations 19 . While such experiments would be extremely cost prohibitive if run as individual 10x Genomics lanes, we reasoned that we could multiplex these experiments together using cell "hashing".
We therefore incubated the PBMCs from different donors with a dilution series of antibody concentrations ranging over three orders of magnitude (Methods). Concentrations of CITE-seq antibodies were staggered between the different samples to keep the total amount of antibody and oligo consistent in each sample. After sample demultiplexing, we examined ADT distributions across all concentrations for each antibody (examples in Figure 3A-C), and assessed signal-to-noise ratio by calculating a staining index similar to commonly used metrics for flow cytometry optimization ( Figure 3D) (Methods).
All antibodies exhibited only background signal in the negative control conditions, and very weak signal-to-noise at 0.06 µg/test. We observed that the signal-to-noise ratio for most antibodies began to saturate within the concentration range of 0.5 to 1 µg/test, comparable to the recommended concentrations for flow cytometry ( Figure 3D). This experiment was meant as a proof-of-concept; an ideal titration experiment would use cells from the same donor for all conditions and a larger range of concentrations, but clearly demonstrates how cell "hashing" can be used to rapidly and efficiently optimize experimental workflows.

Cell hashtags enable the discrimination of low quality cells from ambient RNA
Our cell hashtags can discriminate single cells from doublets based on the clear expression of a single HTO, and we next asked whether this feature could also distinguish low quality cells from ambient RNA. If so, this would enable us to reduce our UMI 'cutoff' (previously set at 200), and would allow for the possibility that certain barcodes representing ambient RNA may express more UMI than some true single cells. Most workflows set stringent UMI cutoffs to exclude all ambient RNA, biasing scRNA-seq results against cells with low RNA content, and likely skewing proportional estimates of cell type.
Indeed, when considering 3,473 barcodes containing 50-200 UMI, we recovered 954 additional singlets based on HTO classifications, with 2,432 barcodes characterized as negatives. We classified each barcode as one of our previously determined nine hematopoietic populations (Methods; Figure 1F), and visualized the results on a transcriptomic tSNE embedding, calculated independently for both 'singlet' and 'negative' groups. For predicted singlets, barcodes projected to B, NK, T, and myeloid populations which were consistently separated on tSNE, suggesting that these barcodes represent true single cells ( Figure 3E). In contrast, 'negative' barcodes did not separate based on their forced classification, consistent with these barcodes reflecting ambient RNA mixtures that may blend multiple subpopulations. We therefore conclude that by providing a readout of sample identity that is independent of the transcriptome, cell "hashing" can help recover low quality cells that can otherwise be difficult to distinguish from ambient RNA ( Figure 3F).

DISCUSSION
Here, we introduce a new method for scRNA-seq multiplexing, where cells are labeled with sample-specific "hashtags" for downstream demultiplexing and doublet detection. Our approach is complementary to pioneering genetic multiplexing strategies, with each having unique advantages. Genetic multiplexing does not utilize exogenous barcodes, and therefore does not require alterations to existing workflows prior to or after sample pooling. In contrast, cell "hashing" requires incubation with antibodies against ubiquitously expressed surface proteins, but can multiplex samples with the same genotype. Both methods do slightly increase downstream sequencing costs, due to increased depth or read length needed to identify SNPs (genetic approaches), or sequencing of HTO libraries (cell "hashing"; approximately 5% of transcriptome sequencing costs). We believe that researchers will benefit from both approaches, enabling multiplexing for a broad range of experimental designs. In particular, we envision that our method will be most useful when processing genetically identical samples subjected to diverse perturbations (or experimental conditions/optimizations, as in our titration experiment), or to reduce the doublet rate when running cells from a single sample.
By enabling the robust identification of cell multiplets, both cell "hashing" and genetic multiplexing allow the "super loading" of scRNA-seq platforms. We demonstrate this in the context of the 10x Genomics Chromium system, but this benefit applies to any single cell technology that relies on Poisson loading for cell isolation. The per-cell cost savings for library preparation can therefore be significant, approaching an order of magnitude as the number of multiplexed samples increases. Notably, cell "hashing" enables even a single sample to be highly multiplexed, as cells can be split into an arbitrary number of pools. As clearly discussed in Kang et al 13 , savings in library prep are partially offset by reads originating from multiplets, which must be sequenced and discarded. Still, as sequencing costs continue to drop, and experimental designs seek to minimize technology-driven batch effects, multiplexing should facilitate the generation of large scRNA-seq and CITE-seq datasets. Informatic detection of multiplets based on transcriptomic data also remains an important challenge for the field, for example, to identify doublets originating from two cells within the same sample.
In our current study, we used a pool of antibodies directed against highly and ubiquitously expressed lymphocyte surface proteins as the vehicle for our HTOs. This strategy aimed to mitigate the possibility that stochastic or cell-type variation in expression of any one marker would introduce bias in HTO recovery. Going forward, we expect a more universal pool of antibodies directed against ubiquitously expressed markers to be used as a universal cell "hashing" reagent for studies beyond the hematopoietic system. With the increasing interest in single nucleus sequencing 20 , an additional set of "hashing" reagents directed against nuclear proteins would further generalize this approach. Beyond antibody/epitope interactions, cell or nuclei "hashing" could also be performed using any other means of attaching an oligo to a cell or nucleus, including other protein:protein interactions, aptamers 21 , or direct chemical conjugation of oligos to cells or nuclei. These improvements will further enable multiplexing strategies to generalize to diverse experiments regardless of species, tissue, or technology.    Normalized ADT Antibody amount (μg)

Figure 3 | Cell "hashing" enables efficient experimental optimization and identification of low quality cells. A-C)
We performed a titration series to assess optimal staining concentrations for a panel of CITE-seq immunophenotyping antibodies. Normalized ADT counts for CD8 (A), CD45RA (B) and CD4 (C) are depicted for the different concentrations used per test. D) Titration curve, depicting the staining index (SI; Methods) for these three antibodies across the titration series. The signal/noise ratio for these antibodies begins to saturate at levels similar to manufacturer recommended staining concentrations typical for flow cytometry antibodies. E) Cells with low UMI counts can be distinguished from ambient RNA using HTO classifications. Classified singlets group into canonical hematopoietic populations. F) Barcodes classified as 'negative' do not group into clusters, and likely represent 'empty' droplets containing only ambient RNA.

DATA AVAILABILITY
Sequencing data is available from the Gene Expression Omnibus under accession GSE108313. Computational tools for classifying cells based on HTO levels will be released as part of the Seurat R package for single cell analysis, available at http://www.satijalab.org/seurat

ACKNOWLEDGEMENTS
We acknowledge members of the NYGC Technology Innovation and Satija labs for critical discussions and support. We thank M. Coppo, S. Fennessey, B. Baysa and S. Pescatore at NYGC for sequencing support. This work was supported by the Chan Zuckerberg Initiative (HCA-A-1704-01895, to RS and PS), NIHR21-HG-009748 (to PS), and an NIH New Innovator Award (DP2-HG-009623, to RS).

PBMC genotyping
Peripheral blood mononuclear cells were obtained from AllCells (USA). Genomic DNA was purified using the All-prep kit (Qiagen, USA) and genotyped using the Infinium core exome 24 array (Illumina, USA) according to manufacturer's instructions.
Cell culture HEK293T (human) and NIH-3T3 (mouse) cells were maintained according to standard procedures in Dulbecco's Modified Eagle's Medium (Thermo Fisher, USA) supplemented with 10% fetal bovine serum (Thermo Fisher, USA) at 37˚C with 5% CO2. [clone: HI111]) and were covalently and irreversibly conjugated to HTOs by iEDDA-click chemistry as previously described 18 . In short, antibodies were washed into 1X borate buffered saline (50 mM borate, 150 mM NaCl pH 8.5) and concentrated to 1 mg/ml using an Amicon Ultra 0.5 ml 30 kDa MWCO centrifugal filter (Millipore). Methyltetrazine-PEG4-NHS ester (Click Chemistry Tools, USA) was dissolved in dry DMSO and added as a 30-fold excess to the antibody and allowed to react for 30 minutes at room temperature. Residual NHS groups were quenched by the addition of glycine and unreacted label was removed via centrifugal filtration. 5'-amine HTOs were ordered from Integrated DNA Technologies (USA) and reacted with a 20fold excess of trans-cyclooctene-PEG4-NHS (Click Chemistry Tools, USA) in 1X borate buffered saline supplemented with 20% DMSO for 30 minutes. Residual NHS groups were quenched by the addition of glycine and residual label was removed by desalting (Bio-Rad Micro Bio-Spin P6). Antibody-oligo conjugates were formed by mixing the appropriate labeled antibody and HTO and incubating at room temperature for at least 1 hour. Residual methyltetrazine groups on the antibody were quenched by the addition of trans-cyclooctene-PEG4-acid and unreacted oligo was removed centrifugal filtration using an Amicon Ultra 0.5 ml 50 kDa MWCO filter (Millipore, USA).

Antibody Titration Series
To test optimal concentration of Antibody-Oligo conjugates provided by BioLegend (USA) per CITE-seq experiment, we tested 5 µg, 3 µg, 1 µg, 0.5 µg, 0.25 µg, 0.06 µg, and 0 µg for each conjugate. Titrations were staggered over the different batches to keep the total concentration of antibodies and oligos consistent between conditions (see

CITE-seq on 10x Genomics instrument
Cells were 'stained' with hashtagging antibodies and CITE-seq antibodies as described for CITE-seq 17 . 'Stained' and washed cells were loaded into 10x Genomics single cell 3' v2 workflow and processed according to manufacturer's instructions up until the cDNA amplification step (10x Genomics, USA). 2 pmol of HTO and ADT additive oligonucleotides were spiked into the cDNA amplification PCR and cDNA was amplified according to the 10x Single Cell 3' v2 protocol (10x Genomics, USA). Following PCR, 0.6X SPRI was used to separate the large cDNA fraction derived from cellular mRNAs (retained on beads) from the ADT-and hashtag-containing fraction (in supernatant). The cDNA fraction was processed according to the 10x Genomics Single Cell 3' v2 protocol to generate the transcriptome library. An additional 1.4X reaction volume of SPRI beads was added to the ADT/hashtag fraction to bring the ratio up to 2.0X. Beads were washed with 80% ethanol, eluted in water, and an additional round of 2.0X SPRI performed to remove excess single stranded oligonucleotides from cDNA amplification. After final elution, separate PCRs were set up to generate the CITEseq ADT library (SI-PCR and RPI-x primers), and the hashtag library (SI-PCR and D7xx_s). A detailed and regularly updated point-by-point protocol for CITE-seq, cell-hashtagging, and future updates can be found at www.cite-seq.com

D7xx_s
CAAGCAGAAGACGGCATACGAGATxxxxxxxxGTGACTGGAGTTCAGACGTGT*G*C x: Barcode or index sequence B: T,G,C, not A *: Phosphorothioate bond

Single-cell data processing
Fastq files from the 10x libraries with four distinct barcodes were pooled together and processed using the standard Drop-seq pipeline (Drop-seq tools v1.0, McCarroll Lab). Reads were aligned to the hg19-mm10 concatenated reference, and we included the top 50,000 cell barcodes in the raw digital expression matrix as output from Drop-seq tools. For ADT and HTO quantification, we implemented our previously developed tag quantification pipeline 17 as a python script, available at https://github.com/Hoohm/CITE-seq-Count, and run with default parameters (maximum hamming distance of 1).

Demultiplexing with genotyping data using demuxlet
We first generated a VCF file that contained the individual genotype (GT) from the Infinium core exome 24 array output, using the PLINK command line tools (version 1.07). This VCF file (which contained genotype information for the 8 PBMC donors as well as HEK cells), and the tagged bam file from Drop-seq pipeline were used as inputs for demuxlet 13 , with default parameters.

Single-cell RNA data processing
Normalization and downstream analysis of RNA data were performed using the Seurat R package (version 2.1, Satija Lab) which enables the integrated processing of multi-modal (RNA, ADT, HTO) single cell datasets 22,23 . We collapsed the joint-species RNA expression matrix to only include the top 100 most highly expressed mouse genes (along with all human genes) using the CollapseSpeciesExpressionMatrix function.
We first considered a set of 22,119 barcodes where we detected at least 200 UMI in the transcriptome data. Since the HEK and 3T3 cells were not labeled with HTOs, we identified these cells based on their transcriptomes. We performed a low-resolution pre-clustering by performing PCA on the 500 most highly expressed genes, followed by Louvain-Jaccard clustering on a distance matrix based on the first five principal components [24][25][26] . Based on this clustering, we identified 248 3T3 cells and 3,401 HEK cells, with the remainder representing PBMCs.
As a separate test of HEK identity, we examined the demuxlet genotype for possible HEK cells. We observed 1,668 barcodes classified as HEK by the demuxlet algorithm, but whose transcriptomes clustered with PBMCs. These cells expressed ten-fold fewer UMI compared to transcriptomically-classified HEK cells, and did not express HEK-specific transcripts (i.e. NGFRAP1), both consistent with a PBMC identity. We therefore excluded these barcodes from all further analysis.

Classification of barcodes based on HTO levels
HTO raw counts were normalized using centered log ratio (CLR) transformation, where counts were divided by the geometric mean of an HTO across cells, and log-transformed: Here ! " denotes the count for a specified HTO in cell -, . is the total cell number, /01 denotes the natural log. Pairwise analysis of either normalized or raw HTO counts ( Figure 1B) revealed mutually exclusive relationships, though determining the exact cutoffs for positive and negative signals required further analysis. We reasoned that if we could determine a background distribution for each HTO based on 'negative' cells, outliers from this distribution would represent positive signals.
To assist in the unsupervised identification of 'negative' cells, we performed an initial k-medoids clustering for all cells based on the normalized HTO data. We set k=9, and observed (as expected) that eight of the clusters were highly enriched for expression of a particular HTO, while the ninth cluster was highly enriched for cells with low expression of all HTO. This represents an initial solution to the de-multiplexing problem that suggests likely populations of 'positive' and 'negative' cells for statistical analysis.
Following clustering, we performed the following the following procedure independently for each of the 8 HTOs. We identified the k-medoids cluster with the highest average HTO expression, and excluded these cells. We next fit a negative binomial distribution to the remaining HTO values, after further excluding the highest 0.5% values as potential outliers. We calculated the q=0.99 quantile of the fitted distribution, and thresholded each cell in the dataset based on this HTO-specific value.
We used this procedure to determine an 'HTO classification' for each barcode. Barcodes that were positive for only one HTO were classified as singlets. Barcodes that were positive for two or more HTO were classified as doublets, and assigned sample IDs based on their two most highly expressed HTO. Barcodes that were negative for all eight HTO were classified as 'negative'.
We expect that barcodes classified as 'singlets' represent single cells, as we detect only a single HTO. However, they could also represent doublets of a PBMC with a HEK or 3T3 cell, as the latter two populations were unlabeled and represent negative controls. Indeed, when we analyzed the 'HTO classification' of cells that were transcriptomically annotated as HEK or 3T3 cells, we found that 73.4% were annotated as 'negative', while 29.2% were annotated as singlets, in complete agreement with expected ratios in our 'super-loaded' 10x experiment. These cells appear in the heatmap in Figure 1C, but all HEK and 3T3 cells were excluded from further analysis.
For two-dimensional visualization of HTO levels ( Figure 1D), we used Euclidean distances calculated from the normalized HTO data as inputs for tSNE. Cells are colored based on their HTO classification as previously described. For visualization and clustering based on transcriptomic data ( Figure 1F), we first performed PCA on the 2,000 most highly variable genes (as determined by variance/mean ratio), and used the distance matrix defined by the first 11 principal components as input to tSNE and graph-based clustering in Seurat ( Figure 1E). We annotated the nine clusters based on canonical markers for known hematopoietic populations.

Comparison with demuxlet
Demuxlet classifications were labeled as singlets (SNG), doublets (DBL) or ambiguous (AMB) according to the BEST column in the *.best output file. In Figure 2E, we plot the posterior probability of a doublet assignment, from the PRB.DBL column in the same file.

Calculation of staining index for antibody titrations.
To assess the optimal staining efficiency for CITE-seq experiments, we considered ADT levels for cells across a range of antibody concentrations, as multiplexed in a titration series. ADT levels were normalized using a CLR transformation of raw counts, using an identical approach to the normalization of HTO levels as previously described.


s2fieldsofstudy: Biology Medicine

text:
Targeted gene correction of FKRP by CRISPR/Cas9 restores functional glycosylation of α-dystroglycan in cortical neurons derived from human induced pluripotent stem cells

Mutations in genes required for functional glycosylation of α-dystroglycan cause a group of congenital muscular dystrophies associated with brain malformations, referred to as dystroglycanopathies. The lack of isogenic, physiology-relevant human cellular models has limited our understanding of the cortical abnormalities in dystroglycanopathies. Here we generate induced pluripotent stem cells (iPSCs) from a severe dystroglycanopathy patient with homozygous mutations in the ribitol-5-phosphate transferase gene, FKRP. We carry out targeted gene correction in FKRP-iPSCs using CRISPR/Cas9-mediated genome editing. We characterise the directed differentiation of FKRP- and corrected-iPSCs to neural stem cells, cortical progenitors and cortical neurons. Importantly, we show that targeted gene correction of FKRP restores functional glycosylation of α-dystroglycan in iPSC-derived cortical neurons. We independently validate this result by showing targeted gene mutation of FKRP disrupts functional glycosylation of α-dystroglycan. This work demonstrates the feasibility of using CRISPR/Cas9-engineered human iPSCs for modelling dystroglycanopathies and provides a foundation for therapeutic development. Highlights Generation of FKRP-iPSCs for modelling cortical abnormalities in dystroglycanopathies Precise gene correction by CRISPR/Cas9-mediated genome editing Directed differentiation of isogenic control and FKRP-iPSC to cortical neurons Functional glycosylation of α-dystroglycan is restored in cortical neurons derived from CRISPR/Cas9-corrected iPSCs Targeted gene mutation of FKRP disrupts functional glycosylation of α-dystroglycan in cortical neurons

Introduction
Post-translational processing of dystroglycan is critical for its function as a cell surface receptor in a variety of fetal and adult tissues. The dystroglycan precursor is cleaved into non-covalently associated α-and β-subunits, forming an integral component of a multiprotein complex. The mature α-dystroglycan is a heavily glycosylated peripheral membrane protein whose molecular weight ranges from 100 to 156 kDa, depending on the tissue-specific glycosylation. In contrast, the βdystroglycan is a 43 kDa transmembrane protein that links to the actin cytoskeleton via interaction with dystrophin (Barresi and Campbell, 2006). Defective O-linked glycosylation of α-dystroglycan is a common pathological hallmark associated with a number of genetic syndromes, encompassing symptoms from muscular dystrophies to ocular defects, cognitive deficits, and cortical malformations (cobblestone lissencephaly) in the central nervous system (CNS). This group of autosomal recessive disorders are commonly referred to as secondary dystroglycanopathies. (Godfrey et al., 2011). Currently there is no cure or effective treatment for dystroglycanopathies.
The effort of identifying causative gene mutations in dystroglycanopathies has shed light on a novel mammalian glycosylation pathway (reviewed in Freeze, 2013;Yoshida-Moriguchi and Campbell, 2015).
Allelic FKRP mutations lead to the widest spectrum of clinical severities, ranging from the mild lateonset limb-girdle muscular dystrophy without neurological deficits (e.g. LGMD2I) to congenital muscular dystrophy with severe CNS abnormalities (e.g. Walker-Warburg syndrome) (Godfrey et al., 2007;Godfrey et al., 2011). Specifically, malformations of cortical development are pathological features at the severe end of the dystroglycanopathy spectrum (Barkovich et al., 2012;Beltran-Valero de Bernabe et al., 2004;Devisme et al., 2012). The absence of physiology-relevant human cellular models has hindered our understanding of mechanisms underlying CNS involvement in dystroglycanopathies and our ability to test potential drug targets in a neural-specific context. In addition, the patient's neural tissue is not easily accessible and there is no appropriate isogenic control for the patient's tissue. Induced pluripotent stem cells (iPSCs), which are remarkably similar to embryonic stem cells (ESCs), can be generated from human somatic tissues (Takahashi et al., 2007). Recent studies have demonstrated success in using patient-specific iPSCs to model a variety of neurodevelopment and neurodegenerative disease Ryan et al., 2013;Shi et al., 2012a). These studies suggest that patient-specific iPSC-derived cortical neurons will allow us to study mechanisms underlying CNS involvement in the severe forms of dystroglycanopathies.
In this study we test the hypothesis that patient-specific iPSC-derived cortical neurons can recapitulate pathological hallmarks in dystroglycanopathies. We generate the first human FKRP-iPSCs from a congenital muscular dystrophy patient with severe CNS abnormalities. We carry out CRISPR/Cas9-mediated genome editing (Cong et al., 2013;Mali et al., 2013) and differentiate isogenic pairs of iPSCs to cortical neurons. We show that, for the first time, targeted gene correction of FKRP restores α-dystroglycan functional glycosylation in iPSC-derived cortical neurons, whereas targeted gene mutation of FKRP disrupts α-dystroglycan glycosylation. Taken together, our isogenic pairs of iPSC-derived cellular models will further elucidate mechanisms underlying the CNS involvement in FKRP-deficient dystroglycanopathies.

Generation and characterisation of FKRP-iPSCs derived from a dystroglycanopathy patient with CNS abnormalities
We obtained dermal fibroblasts from a female individual, previously diagnosed congenital muscular dystrophy with CNS abnormalities, including marked cognitive delay, microcephaly, cerebellar cysts and cerebellar dysplasia. Genetic analysis revealed homozygous FKRP c.1364C>A (p.A455D) mutations in this affected individual. Both parents were heterozygous carriers of this mutation. To generate patient-specific iPSCs, we reprogrammed the fibroblasts with the FKRP c.1364C>A mutations using a six-factor reprogramming technology based on a doxycycline-inducible system (Wang et al., 2011). The initial characterisation of several independent FKRP-iPSC lines by qPCR revealed the gene expression of pluripotency markers, such as NANOG, OCT4 and REX1 ( Figure   S1). Among these patient-specific FKRP-iPSC lines, we focused on the FKRP-iPSC line 1-6, which exhibited the normal karyotype, suggesting these iPSCs are genetically stable ( Figure 1B).
Immunocytochemistry confirmed the expression of pluripotency markers, such as NANOG, OCT4, Tra-1-60 and SSEA4 in the established FKRP-iPSC line ( Figure 1C). In addition, in vitro differentiation of the FKRP-iPSCs formed embryoid bodies with cell types representing all three embryonic germ layers, confirmed by immunocytochemistry in specific cell lineages, such as β-III tubulin (Tuj1) for ectoderm, smooth muscle actin (SMA) for mesoderm and α-fetoprotein (AFP) for endoderm ( Figure 1D).

Targeted gene correction of FKRP-iPSCs using CRISPR/Cas9 mediated genome editing
A common issue of patient-specific iPSCs in disease modelling is the lack of appropriate isogenic control cells, causing concerns about the effect of genetic backgrounds on phenotypic variability. To overcome this issue, we applied a precise genome editing strategy to correct the FKRP c.1364C>A (p.A455D) mutation using site-specific endonuclease CRISPR/Cas9 stimulated homologous recombination (Cong et al., 2013;Mali et al., 2013), which consists a single guide RNA (sgRNA), the Cas9 nuclease and a donor targeting vector. To avoid potential off-target effects, we used an optimised computational algorithm (http://crispr.mit.edu) to identify appropriate sgRNAs for utilizing the CRISPR/Cas9 to generate DNA double strand breaks (DSB) near the FKRP mutation ( Figure   2A). This sgRNA has high on-target and low off-target scores (Table S1). To facilitate the screening process for homologous recombination events, we used an approach based on the transposon piggyBac (PGK-puro∆tk) selection cassette in the targeting donor vector, which enables both positive and negative selections (Yusa, 2013).
We PCR-amplified two 1-kb fragments flanking the CRISPR/Cas9 target site close to the FKRP c.1364C>A allele, which was simultaneously corrected (FKRP c.1364C). The two fragments (homology left and right arms) flanking a piggyBac (PGK-puro∆tk) selection cassette were assembled together into a targeting donor vector ( Figure 2B). DNA sequences at the junctions between piggyBac (PGK-puro∆tk) selection cassette and homology arms were modified to accommodate TTAA sequences, yet coding the same amino acids after excision of the selection cassette ( Figure 2B). We electroporated the site-specific CRISPR/Cas9 plasmids with the targeting donor vector (FKRP c.1364C) into the iPSCs. By positive selection with puromycin, iPSCs with an integrated donor vector formed puromycin-resistant clones, which were picked for rapid PCR genotyping ( Figure S2). We identified 3 homozygously and 27 heterozygously targeted independent clones (Table 1), which were confirmed by sequencing (data not shown). To excise the piggyBac (PGK-puro∆tk) selection cassette, 2 homozygously targeted FKRP (c.1364C)-iPSC clones (5.19 and 3.16) were electroporated with piggyBac transposase expression plasmid, followed by negative selection in culture media containing 1-(2-Deoxy-2-fluoro-β-D-arabinofuranosyl)-5-iodo-2,4(1H,3H)-Pyrimidinedione (FIAU), a thymidine analogue that would be processed to toxic metabolites in the presence of piggyBac (PGK-puro∆tk). PCR genotyping identified 11 biallelicly corrected-iPSC clones that had the selection cassette completely excised without re-integration ( Figure S3 and Table 1). The biallelicly corrected-iPSC clones (5D17, 5D23 and 3B17) were sequenced to confirm a clean correction of the FKRP mutation and the engineered selection cassette excision site ( Figure   2C). The biallelicly corrected iPSC lines retained normal karyotypes ( Figure 2D) and pluripotency (data not shown). In addition, we sequenced the top 5 potential off-target sites ( Table S1) and confirmed that no mutations were introduced during genome editing (data not shown).

corrected-iPSCs
To investigate the potential of iPSCs for modelling neural pathogenesis in dystroglycanopathies, we used a serum-free neural induction medium to derive primitive neural stem cells (NSCs) from iPSCs (Yan et al., 2013). Immunocytochemistry confirmed that NSCs derived from FKRP-and CRISPR/Cas9 corrected-iPSC lines express classic NSC markers, including SOX1, SOX2 and nestin ( Figure 3A,B). In terms of the efficiency of neural induction, we did not observe discernable difference between NSCs derived from FKRP-and three corrected-iPSC lines, 5D17, 5D23 and 3B17 ( Figure 3C,D). Quantification of SOX1 and SOX2 positive NSCs showed >99% efficiency from both FKRP-and three corrected iPSC lines ( Figure 3C,D).
Subsequently, we directed the differentiation of FKRP-and corrected-NSC lines towards cortical projection neurons using a well-established protocol, which recapitulates important stages in human cortical development (Shi et al., 2012b). After switching to the Neural Maintenance Medium for one week, we confirmed the identity of FKRP-and corrected-iPSC derived cortical stem and progenitor cells, which express classic cortical stem cell markers, PAX6, OTX2 and vimentin ( Figure

Functional glycosylation of α-dystroglycan is restored in cortical neurons derived from CRISPR/Cas9 corrected-iPSCs
Next, we investigated whether we could detect functional glycosylation of α-dystroglycan in cortical neurons derived from our corrected-iPSC lines. To do this, we performed immunoblot using the IIH6 antibody, which recognises the laminin-binding glyco-epitope on α-dystroglycan (Michele et al., 2002). Wildtype mouse muscle and brain lysates were used as positive controls to show differential glycosylation of α-dystroglycan in a tissue-specific manner. In addition, we included lysate of cortical neurons from a non-isogenic, wildtype iPSC20 line (hereinafter called WT-iPSCs) (Wang et al., 2011). We showed that the molecular weight of glycosylated α-dystroglycan in WT-iPSC derived cortical neurons is similar to that in mouse brain lysate (~120 kDa) and less than that in muscle lysate (~156 kDa) ( Figure 4A), consistent with previously reported tissue-specific glycosylation of αdystroglycan ( Barresi and Campbell, 2006). Furthermore, IIH6 reactivity on immunoblot was almost not detected in cortical neurons derived from FKRP-iPSCs ( Figure 4A and B). In contrast, cortical neurons derived from CRISPR/Cas9 corrected-iPSC lines clearly showed IIH6 reactivity, indicating restored glycosylation of α-dystroglycan ( Figure 4B).
Following the detection of glycosylated α-dystroglycan in corrected cortical neurons, we investigated whether the laminin-binding activity is associated with the IIH6 glyco-epitope on α-dystroglycan. We confirmed that laminin-binding is disrupted in cortical neurons derived from FKRP-iPSCs ( Figure   4D), whereas cortical neurons derived from corrected-iPSC lines showed strong laminin-binding activities ( Figure 4D). Together, these results demonstrated a restoration of functional glycosylation of α-dystroglycan in CRISPR/Cas9-corrected corrected cortical neurons.

Targeted gene mutation of FKRP disrupts functional glycosylation of α-dystroglycan in cortical neurons derived from iPSCs
To independently validate the results from our FKRP-and corrected-iPSC derived cortical neurons, we used the same genome editing strategy ( Figure S4A) to knock-in the FKRP c.1364C>A (p.A455D) mutation into the WT-iPSCs (Wang et al., 2011). We constructed a donor targeting vector carrying the FKRP mutation for CRISPR/Cas9-mediated homologous recombination. We electroporated the site-specific CRISPR/Cas9 plasmids with the targeting donor vector (FKRP c.1364C>A) into the WT-iPSCs ( Figure S4B). Using puromycin positive selection and PCR genotyping ( Figure S2), we identified 2 homozygously targeted clones (a15 and c43), which were expanded for selection cassette excision (Table 2). Following FIAU negative selection and PCR genotyping ( Figure S3), we identified 2 biallelicly FKRP mutated-iPSC clones (C29 and C31) that have the selection cassette completely excised without re-integration ( Table 2). The FKRP mutated-iPSC clones (C29 and C31) were sequenced to confirm the biallelic knock-in of FKRP c.1364C>A (p.A455D) mutations and the engineered selection cassette excision site ( Figure S4C). The wildtype-and FKRP mutated-iPSC lines were differentiated to NSCs ( Figure S5) and subsequently cortical neurons for functional analysis. Compared with cortical neurons derived from WT-iPSCs, targeted gene mutation of FKRP disrupted the IIH6 reactivity ( Figure 4A and C) in cortical neurons derived from FKRP mutated-iPSC lines, indicating loss of functional glycosylation of α-dystroglycan.
Together, our results demonstrated that target gene mutation of FKRP by CRISPR/Cas9-mediated genome editing could recapitulate pathological hallmarks of dystroglycanopathy in cortical neurons.

Discussion
The combination of patient-specific iPSCs and CRISPR/Cas9-mediated genome editing has enormous potential for disease modelling, as well as for drug discovery and development. We report here that CRISPR/Cas9-induced homologous recombination together with the piggyBac positive/negative selection cassette is a powerful and versatile strategy, which allows precise modification of the mammalian genome at a single base-pair level without leaving footprints. For the first time, we demonstrate a precise genome editing at the FKRP locus in human iPSCs. Using our genome editing strategy, we were able to achieve 7-10% bialleic targeting efficiency at the FKRP locus (Table 1) and no mutations were observed at the top 5 predicted off-target sites (data not shown). According to Leiden Open Variation Database (http://www.lovd.nl/), at least 95% reported FKRP DNA variants are missense mutations (969 out of 1018 cases). Homozygous null FKRP mutations are exceptionally rare and a single case of Walker-Warburg syndrome with a mutation affecting the translational start site was reported (Van Reeuwijk et al., 2010). Most patients carry heterozygous or homozygous FKRP mutations or, less frequently, a combination of a nonsense or frameshift mutation with a missense mutation (5 out of 38 patients in our research cohort). Among these mutations, FKRP c.826C>A (L276I) mutation is one of the most common variants and causes a mild form of dystroglycanopathy (LGMD2I). Our genome editing strategy can be applied to precisely correct these FKRP missense mutations and by implication small insertion/deletion mutations (INDELs), as well as similar type of mutations in other dystroglycanopathy genes. Recent advances in the delivery of Cas9-sgRNA ribonucleoproteins (Cas9 RNPs) (Liu et al., 2015), may further improve the efficiency of biallelic gene targeting. The advantage of using Cas9 RNPs for genome editing is the rapid Cas9 cleavage action and protein turnover in the cells within 24 h of delivery, which increases the gene targeting efficiency and reduces the off-target mutation rate that is critical for future therapeutic application.
The process of cellular reprogramming from fibroblasts to iPSCs followed by directed differentiation from iPSCs to NSCs and subsequently to cortical neurons involves dramatic changes of gene expression profile of a cell. The functional glycosylation of α-dystroglycan is a consequence of a complex biosynthetic pathway orchestrated by at least 17 known enzymes to form the ECM ligandbinding moiety (Yoshida-Moriguchi and Campbell, 2015). Our results indicate that genes involved in functional glycosylation of α-dystroglycan are expressed during in vitro corticogenesis. Interestingly, the molecular weight of α-dystroglycan varies due to tissue-specific O-glycosylation (Barresi and Campbell, 2006). Consistent with previous studies, we show that the molecular weight of glycosylated α-dystroglycan in cortical neurons derived from human iPSCs is similar to that in the mouse brain and less than that in mouse muscle (Figure 4). Importantly, we demonstrate that Mutations in FKRP and other known causative genes together account for approximately 50-60% of severe forms of dystroglycanopathies (Roscioli et al., 2012;Vuillaumier-Barrot et al., 2012).
Previously a zebrafish based study showed cooperative interaction between ISPD, FKTN and FKRP (Roscioli et al., 2012). Recent studies demonstrated the enzymatic function of ISPD, FKTN and FKRP, which synergistically add tandem ribito-5-phosphate onto α-dystroglycan (Gerin et al., 2016;Kanagawa et al., 2016). Despite these advances, the effects of FKRP mutations and defective glycosylation of α-dystroglycan in a disease context still remain poorly understood. Our isogenic pairs of control-and FKRP-iPSC derived cortical neurons will further elucidate molecular mechanisms underlying cortical development in health and disease using a systems-based approach, similar to transcriptome analysis of in vitro corticogenesis from human ESCs (van de Leemput et al., 2014). Importantly, this work will not only elucidate the role of glycosylated dystroglycan in the brain, but will also have significant impacts on studying its role in other tissues.
Apart from muscle degeneration, dilated cardiomyopathy is a frequent associated feature of FKRPdeficient dystroglycanopathy (Mercuri and Muntoni, 2013). The isogenic pairs of human iPSCs generated in this study have enormous potential in many other research avenues. In addition to neural differentiation, the isogenic pairs of control-and FKRP-iPSC lines can be used for myogenic and cardiac differentiation with established protocols (Bellin et al., 2013;Chal et al., 2016) for studying pathological mechanisms underlying muscle degeneration and dilated cardiomyopathy in muscular dystrophy patients. In addition, one can envisage the use of progenitor cells derived from CRISPR/Cas9-corrected iPSCs for autologous cell replacement therapies in the future. The advantage of using autologous, CRISPR/Cas9-corrected progenitor cells is the reduced risk of immunological rejection and need for immunosuppression.
In conclusion, we have established two isogenic pairs of human iPSC models for a severe form of FKRP-deficient dystroglycanopathy. A significant impact of this study is that other human iPSCbased dystroglycanopathy models will be generated. Collectively, these iPSC models will provide powerful in vitro platforms that can be exploited for setting up high-content drug screens using quantifiable phenotypes as readouts. Importantly, while more than 90% of drugs fail during development (DiMasi et al., 1991), our isogenic pairs of human iPSC lines will provide an invaluable resource for systematic chemical and genetic screens capable of reverting pathophysiological defects in physiology-relevant cellular models. This will facilitate the drug discovery and development for treating dystroglycanopathies.     neurons is similar to that in the mouse brain (~120 kD) and lower than that in the mouse muscle (~156kD). IIH6 reactivity was almost not detected in cortical neurons derived from FKRP-iPSCs.   (Wang et al., 2011). In vitro differentiation and analysis were carried out as described (Wang et al., 2011).

Vector construction and CRISPR/Cas9 mediated genome editing
For targeted gene correction, we used Gibson Assembly (New England BioLabs) to construct the targeting donor vector. Briefly, the 1-kb left and right homology arms (LHA and RHA) were PCRamplifed from parental FKRP fibroblast. The FKRP c.1364C>A (p.A455D) mutation on the LHA was simultaneously corrected with a modified primer (Table S2). The piggyBac (PGK-puro∆tk) selection cassette and vector backbone were PCR-amplified from the pMCS-AAT_PB-PGKpuroTK plasmid (Yusa et al., 2011). The four PCR fragments have 40-bp overlappings from end to end (primers are listed in Table S2) and joined together using Gibson Assembly Master Mix. The hCas9 plasmid was a gift from George Church (Addgene plasmid # 41815) (Mali et al., 2013). Short DNA fragments containing the FKRP sgRNA target sequence (Table S1) were generated by annealing two primers (Table S2), which create two overhangs ready for cloning into BsaI-digested p1261_U6_BsaI_gRNA_plasmid (kind gift of Sebastian Gerety, Wellcome Trust Sanger Institute).
The expression of sgRNA is under the control of U6 promoter. The cell suspension was transferred to a cuvette and electroporated using Amaxa Nucleofection 2b device with program B16. For targeted gene mutation, the same strategy was used to construct the donor targeting vector carrying the FKRP c.1364C>A (p.A455D) mutation on LHA using primers in Table S2.
When reaching 90% confluence (approximately within 3-4days), cells were re-seeded (60,000/cm 2 ) in Neural Maintenance Medium (with 5 µM Y-27632 dihydrochloride for 24 h), followed by media change every other day. In vitro cortigenesis occurs in the following weeks. Cultured cells were then harvested or fixed at specific time points for further analysis.

Immunocytochemistry
Cells were fixed with 4% PFA for 15 mins at room temperature (RT). Prior to immunocytochemistry, cells were permeabilized with 0.1% Triton/phosphate-buffered saline (PBS) for 15min at RT and blocked with either 10% goat serum/PBS or 10% horse serum/PBS. Primary antibodies used were: OCT4 ( SuperSignal West Pico Chemiluminescent Substrate (Thermo Fisher) was applied to PVDF membranes and signals were visualized using a Bio-Rad ChemiDoc MP imaging system.

Laminin overlay analysis
Protein samples were extracted, resolved and transferred to PVDF membranes as described above.
The PVDF membrane was then blocked with 5% skimmed milk in laminin binding buffer (LBB), containing 10 mM triethanolamine, 140 mM NaCl, 1 mM MgCl 2 , 1 mM CaCl 2 (pH adjusted to 7.6), followed by incubation with laminins from Engelbreth-Holm-Swarm murine sarcoma basement membrane (Sigma, L2020) in LBB with a final concentration of 5 µg/ml at 4 o C overnight. The PVDF membrane was then incubated with the pan-laminin antibody (1:1000; Sigma, L9393), washed with 1x LBB with 0.1% Tween and incubated with HRP conjugated anti-rabbit IgG (Jackson laboratories). Signal detection using chemiluminescence substrate was performed as above.


extfieldsofstudy: Biology Medicine

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Fit for genomic and proteomic purposes: Sampling the fitness of nucleic acid and protein derivatives from formalin fixed paraffin embedded tissue

The demand for nucleic acid and protein derivatives from formalin-fixed paraffin-embedded (FFPE) tissue has greatly increased due to advances in extraction and purification methods, making these derivatives available for numerous genomic and proteomic platforms. Previously, DNA, RNA, microRNA (miRNA), or protein derived from FFPE tissue blocks were considered “unfit” for such platforms, as the process of tissue immobilization by FFPE resulted in cross-linked, fragmented, and chemically modified macromolecules. We conducted a systematic examination of nucleic acids and proteins co-extracted from 118 FFPE blocks sampled from the AIDS and Cancer Specimen Resource (ACSR) at The George Washington University after stratification by storage duration and the three most common tumor tissue types at the ACSR (adenocarcinoma, squamous cell carcinoma, and papillary carcinoma). DNA, RNA, miRNA, and protein could be co-extracted from 98% of the FFPE blocks sampled, with DNA and miRNA “fit” for diverse genomic purposes including sequencing. While RNA was the most labile of the FFPE derivatives, especially when assessed by RNA integrity number (RIN), it was still “fit” for genomic methods that use smaller sequence lengths, e.g., quantitative PCR. While more than half of the protein derivatives were fit for proteomic purposes, our analyses indicated a significant interaction effect on the absorbance values for proteins derived from FFPE, implying that storage duration may affect protein derivatives differently by tumor tissue type. The mean absorbance value for proteins derived from more recently stored FFPE was greater than protein derived from older FFPE, with the exception of adenocarcinoma tissue. Finally, the fitness of one type of derivative was weakly associated with the fitness of derivatives co-extracted from the same FFPE block. The current study used several novel quality assurance approaches and metrics to show that archival FFPE tissue blocks are a valuable resource for contemporary genomic and proteomic platforms.

Introduction
The demand for high-quality molecular derivatives from archived formalin-fixed paraffinembedded (FFPE) tissue from biorepositories has greatly increased in recent years due to the low cost, high-throughput, and accessibility of contemporary genomic and proteomic platforms [1,2]. This is despite the fact that FFPE blocks are often considered a poor source for genomic and proteomic material. As with many biobanked samples [3], the requirements of FFPE for use in contemporary research platforms were not known when the tumor tissues were originally fixed, a particularly critical issue for nucleic acids and proteins, which are tightly cross-linked into the FFPE matrix. While FFPE immobilization preserves tissue morphology, including cytological details and the immunoreactivity of tissue antigens, this crosslinking has deleterious effects on the embedded nucleic acids and proteins, such as chemical modifications, degradation, and even sequence alterations [4]. However, recent advances in the extraction and purification of nucleic acids and proteins from FFPE blocks have now made these derivatives highly accessible for genomic and proteomic analyses [5][6][7][8], though the "quality" or the "fitness" of these derivatives remains unpredictable. Herein, a fit-for-purpose approach was used to assess the "fitness" of DNA, RNA, miRNA, and protein derivatives co-extracted from FFPE blocks archived in the AIDS and Cancer Specimen Resource (ACSR) at The George Washington University (GWU). The objective of this Fit For Purpose (FFP) study was to provide biobanks with a framework to apply in similar studies to assess nucleic acids and protein derivatives from FFPE tissue blocks [3,9] and to produce evidence for the critical role of biorepositories as partners in cancer research.

Study site
This study was done under the approval of the George Washington University Institutional Review Board (IRB#069498). FFPE blocks were sampled from the ACSR at GWU, where they were acquired from diverse geographical locations from 1990 to 2013 and stored at 21-23˚C in a temperature monitored room. The ACSR database, which includes data for the storage location and the annotation of the FFPE blocks, was used to generate the stratified random sample (described in detail below).

Power and sample size calculations
A sample size of 120 FFPE cases was determined by several logistical factors, including labor, reagents, and availability of adenocarcinoma, papillary carcinoma, and squamous cell carcinoma blocks (the three most common tumor types in FFPE) in the ACSR at GWU. Statistical computation showed this sample size was sufficient to detect a twofold difference in the "fitness" between FFPE blocks from cases stored for less than 11 years (2002-2013, n = 60 cases) and greater than 11 years (1990-2001, n = 60 cases) assuming an alpha = 0.05 and a beta = 0.20.

Block inclusion criteria, stratification, and randomization
The ACCESS database of the ACSR at GWU was used to search for FFPE blocks by cancer case to avoid selecting multiple FFPE blocks from the same individual. A sampling frame generated from listing available FFPE blocks was stratified by storage duration (11 year intervals starting at 1990) and then by the three most common tumor tissue types in the ACSR at GWU. Simple random sampling without replacement was conducted within each of the six strata (three tissue types x two time intervals). If the first FFPE block from a sampled case did not meet the inclusion criteria described below, the next available FFPE block from the same case was sampled. If a case did not have any blocks meeting the inclusion criteria, the next case from the randomized list was selected until the sample size of 20 FFPE blocks for each storage interval and each tumor tissue type was obtained. An FFPE block had to have sufficient material to generate four 10 μm sections, with the available tissue occupying an area of at least 4 mm 2 to be included in the study.

Sectioning
Each FFPE block was faced off and trimmed on a Microm HM315 microtome (Walldorf, Germany), with the first two 5 μm sections discarded before a 10 μm section was cut for use in the study. Each 10 μm section was collected into separate sterile 1.5 mL Eppendorf tubes and stored in a cryogenic box at −20˚C. Of the FFPE blocks sampled, 120 had adequate tumor tissue by visual examination for inclusion into the study; however, after nucleic acid and protein extraction and purification only 118 (98%) of the 120 blocks had tissue that could be analyzed for absorbance and concentration as shown in Fig 1.   Fig 1. Fit for genomic and proteomic purposes by steps (numbered yellow boxes). In step 1, the ACCESS database of the ACSR at GWU was used to construct a sampling frame of available FFPE blocks by cancer case, which were the sampling units used to avoid selecting multiple FFPE blocks from the same individual. In step 2, the sampling frame of FFPE blocks was stratified by intervals of 11 years of storage (1990-2001 and 2002-2013, inclusive) and then the three tumor tissue types with the highest frequency of FFPE in the ACSR at GWU. Simple random sampling without replacement was conducted in each stratum until the targeted sample size of 20 FFPE blocks per storage duration and tumor tissue type was reached. In step 3, commercial kits were employed to extract nucleic acids and protein from 10 μm FFPE sections from each block; a separate FFPE section was used for each type of nucleic acid or protein extraction. In step 4, an initial assessment for the presence of the nucleic acid or protein was conducted by ultraviolet absorbance (UV). In step 5, the purity and concentration of nucleic acid and protein extracts were determined by a SpectraDrop Micro-Volume Microplate with a SPECTRAmax 384PLUS plate reader and SoftMax Pro v6.4.1 software for device control and data analysis. In step 6, nucleic acid and protein derivatives were assigned to fitness categories as described in the Methods section. In step 7, the fitness of each FFPE block was assessed by ranking the combined fitness of the derivatives as follows: FFPE blocks that met the "Fit Nucleic Acids and Proteins for Diverse Analyses" requirements included blocks in which all four derivatives (DNA, RNA, miRNA, and protein) were "Fit". FFPE blocks that were categorized as "Fit Nucleic Acids for Diverse Genomic Analyses" included blocks that were determined to have "Fit" nucleic acid derivatives only. FFPE blocks that had one or two "fit" or "above fit" derivative out of the three nucleic acid derivatives and "unfit", "fit" or "above fit" protein derivatives, were considered "Fit for a Specific Genomic or Proteomic Analysis". In step 8, if an FFPE block had no "Fit" molecular derivatives, it was considered a "Bad Block".

Extraction and purification of nucleic acids and proteins
Standardized approaches and kits were used for nucleic acid extraction with a single 10 μm FFPE section used for each extraction. Specifically, genomic DNA was extracted from a single 10 μm FFPE section according to manufacturer's instructions using the QIAamp1 DNA FFPE Tissue kit (Qiagen, Valencia, CA) and eluted into a total volume of 50 microliters of ATE (supplied) buffer. A single 10 μm FFPE section was used to extract both miRNA using the miRNeasy FFPE kit (Qiagen, Valencia, CA) and RNA using the RNeasy1 FFPE kit (Qiagen, Valencia, CA) according to manufacturer instructions and both derivatives individually eluted to 16 microliters of RNase-free water. A third 10 μm FFPE section was used to extract protein using the Qproteome1 FFPE Tissue kit (Qiagen, Valencia, CA) according to manufacturer's instructions. All samples were maintained on ice prior to absorbance and concentration determination as described below. After analysis, the samples were stored at -70˚C. For protein, an additional protocol was used, in which after the initial assessment of concentration by UV absorbance (A 280 ), the protein preparation was further purified according to the manufacturer's protocol "Extraction of Proteins from FFPE Tissues and Cleanup for 2D-PAGE or MS analysis." This involved continuing with a methanol precipitation step and eventual dissolution of the protein pellet in 20 microliters of PBS. Commercially available kits for each derivative type were used [10].

Analysis of nucleic acids and protein extracts
All nucleic acid and protein preparations were analyzed using a SpectraDrop Micro-Volume Microplate (Molecular Devices, Sunnyvale, CA) with a SPECTRAmax 384PLUS plate reader (Molecular Devices, Sunnyvale, CA) and SoftMax Pro v6.4.1 (Molecular Devices, Sunnyvale, CA) software for device control and data analysis. Briefly, four microliters of each sample were placed on the SpectraDrop Microplate and 0.5 mm path length cover slide. Absorbance measurements were taken at 230 nm, 260 nm, and 280 nm for all samples and Spectrum wavescans stored (220 nm-350 nm with a step size of 4 nm). Absorbance ratios were determined via Soft-Max Pro v6.4.1 software and concentration was determined using default software formulas with the following concentration factors: RNA (40); miRNA (40); and DNA (45). Given the presence of a heterogeneous protein preparation without known extinction coefficients, the absorbance value at 280 nm was used as the only indicator of protein concentration. RNA was also analyzed with a 2100 BioAnalyzer (Agilent Technologies, Santa Clara, CA) for RNA integrity (RIN) using the RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA) and 2100 Expert Software. DNA was subsequently analyzed on a 2200 TapeStation (Agilent Technologies, Santa Clara, CA), utilizing the Genomic DNA ScreenTape (Agilent Technologies, Santa Clara, CA) for quality control analysis of DNA samples with the Agilent 2200 TapeStation Software. All DNA samples (one microliter) were analyzed according to manufacturer's instructions. Results and data obtained were not included in statistical analysis but provided in S1 File.

Ordinal fitness classification
Nucleic acid and protein derivatives were assigned to fitness categories as defined in previous publications [10,11]. The SAS coding to convert continuous variables into mutually exclusive categorical variables can be found in S2 File. Please note that the cutoff values for fitness classification are set at two decimal places of accuracy. For the nucleic acids (DNA, RNA and miRNA), the categories of "unfit", "fit", and "above fit" by the absorbance ratio of A 260/280 ratios are as follows: an A 260/280 ratios < 1.50 or > 2.50 were considered 'unfit"; an A 260/280 ratio between 1.50-1.80 (inclusive of these values) and between 2.20-2.50 (inclusive of these values) were considered "fit"; and an A 260/280 ratio ranging from 1.80-2.20 (exclusive of these values) were considered "above fit" [12] [13]. For proteins, absorbance values obtained at A 280 were used for classification. The classifications of "unfit," "fit" or "above fit" were based upon the expected total protein recovery from a single FFPE section. Protein values were defined as "unfit" when the A 280 was < 1.25; "fit" when A 280 values ranged from 1.25-2.50 (inclusive of these A 280 values); and "above fit" when the protein values > 2.50 A 280 . The concentrations of DNA, RNA, and miRNA were divided into three categories: < 5.00 ng/μL, between 5.00-50.00 ng/μL (inclusive of these concentration values), and > 50.00 ng/μL. While various assays and applications may have sample concentration requirements, the concentration intervals presented above represent sufficient molecular extract for common genomic applications (e.g. PCR, microarray, or next generation sequencing). Ordinal assignment for RNA by RIN value was based on the published literature (see [10,11]) as follows: RIN "unfit" was < 2.00 RIN; "fit" was a RIN within the range of 2.00-3.00 (inclusive of these RIN values); and "above fit" was a RIN > 3.00.

Statistical analysis
Two replicate values were taken for the absorbance and concentration of DNA, RNA, miRNA and protein derivatives from each FFPE block, with the mean of the duplicates used for statistical analyses when both values were positive. In the case when duplicate readings had conflicting values, i.e., one absorbance or concentration had a value that was positive and a value that was negative, the positive value was retained for statistical analyses. In three cases, both duplicate concentration values were negative for a derivative and as such zero was assigned to their corresponding concentration measurements, implying that the sample was "unfit" for that particular derivative. An ordinal categorical classification of overall block fitness is summarized in Table 1.
Box and whiskers plots (Figs 2 and 3) were used to depict the effect of storage duration in 11-year intervals and cancer tumor tissue type on the absorbance and concentration of derivatives derived from FFPE. A two-way analysis of variance (ANOVA) tested the effects of the two factors on these derivatives, followed by multiple pairwise comparisons with a Bonferroni correction when an overall difference among the derivatives was determined to be significant by the ANOVA. Finally, a three-way ANOVA was used to assess differences in absorbance and concentration from FFPE derivative types by cancer tumor tissue type, storage duration by 11-year intervals, and the interactions of these two factors.
In order to statistically assess the fitness of derivatives, Cochran-Mantel-Haenszel (CMH) tests were performed based on the contingency tables shown in Tables 2-5 for each derivative type. These tables evaluated the association between fitness category (i.e. "unfit", "fit" and Table 1. Classification of FFPE block fitness as determined by the quality of nucleic acid and proteins derivatives.

Fitness Definition
Fit Nucleic Acids and Proteins for Diverse Analyses Contains "fit" or "above fit" nucleic acids and protein derivatives in a concentration sufficient for downstream applications Nucleic Acids Fit for Diverse Genomic Analyses Contains a "fit" or "above fit" nucleic acid derivatives (e.g., DNA, RNA and miRNA) but "unfit" protein derivatives Fit for a Specific Genomic or Proteomic Analysis Only one or two "fit" or "above fit" derivative out of the three nucleic acid derivatives and "unfit", "fit" or "above fit" protein derivatives Bad Block (absent of any fit derivatives) Contains nucleic acid or protein derivatives determined to be "unfit" "above fit") and storage duration or fitness category and cancer tumor tissue type. Besides analyzing the effects of storage duration and cancer tumor tissue type on the fitness of a derivative type, the overall fitness of each FFPE block, i.e. the combined fitness of the four derivatives coextracted from the same FFPE block, was investigated using CMH tests. A Spearman rank correlation was used to determine the association between the grouped fitness of derivatives coextracted from the same FFPE block. The continuous measurements of absorbance and concentration were examined as log-transformed values, since the raw data deviated from a normal distribution. Tests were considered statistically significant of P 0.05. Statistical analyses were conducted using SAS software, version 9.3 (SAS Institute Inc., Cary, North Carolina).

Descriptive statistics
DNA from FFPE. No significant differences in mean absorbance value ratios (A 260 / 280 ) were observed for DNA, when derived from FFPE stratified by either storage duration or  Fitness of derivatives from archived formalin fixed paraffin embedded tissue blocks cancer tumor tissue type (Fig 2A and 2B). No significant differences in mean concentration of DNA were observed when stratified by storage duration or cancer tumor tissue type based on a two-way ANOVA (Fig 3A and 3B). Further interrogation of DNA samples were made with the genomic DNA TapeStation and the sizing range (base pair) along with peak (base pair size) of the DNA present in samples is provided in S1 File. This information may be considered supplemental as it is not included in the statistical model used for the Fit for Purpose study.
RNA from FFPE. A significant difference was observed in the mean absorbance values (A 260 / 280 ) for RNA derived from FFPE, when stratified by cancer tumor tissue type when analyzed by two-way ANOVA ( Ã P < 0.04), although no significant differences remained after Bonferroni correction for three tests (Fig 2A and 2B). No significant effect of cancer tumor tissue type or storage duration was observed on the mean concentration of RNA extracted from FFPE when assessed by two-way ANOVA (Fig 3A and 3B). miRNA from FFPE. A significant difference in mean absorbance ratio (A 260 / 280 ) was observed for miRNA derived from FFPE by storage duration in 11 year intervals as determined by two-way ANOVA ( Ã P < 0.03) (Fig 2A and 2B). Subsequent multiple pairwise testing, with a Bonferroni correction, showed that the miRNA derived from recently stored FFPE (2002-2013) had an mean absorbance (A 260 / 280 ) ratio 1.082 times higher than miRNA derived from older FFPE (1990FFPE ( -2001. No significant difference in the mean concentration of miRNA by either cancer tumor tissue type or storage duration as assessed by two-way ANOVA (Fig 3A  and 3B).
Protein from FFPE. No significant differences were observed for the mean absorbance values (A 280 ) for protein derived from FFPE when stratified by cancer tumor tissue type and storage duration as determined by two-way ANOVA (Fig 2A and 2B).
Comparison of DNA, RNA, miRNA and protein co-extracted from FFPE blocks. There were significant differences in mean absorbance values among the four FFPE derivatives when tested by three-way ANOVA ( Ã P < 0.0001) consisting of (1) derivative type, (2) storage duration and (3) cancer type. The absorbance levels of proteins derived from FFPE were much lower than the absorbance levels of the three nucleic acids derived from FFPE. The mean absorbance levels of DNA, RNA and miRNA were 1.34, 1.48 and 1.49 significantly higher than the mean absorbance level of protein, respectively. A significant difference in the mean concentration of nucleic acid derivatives (DNA, RNA, miRNA) was also observed by three-way ANOVA, with the mean concentration levels of DNA derived from FFPE significantly lower ( Ã P < 0.0001 even after Bonferroni correction) compared to the mean concentrations of RNA and miRNA derived from FFPE. The mean concentration levels of RNA and miRNA were 1.67 and 2.20 times higher than DNA. Interaction analyses. The potential for the interaction of storage duration of FFPE and the tumor tissue type on absorbance and concentration values of DNA, RNA, miRNA and protein derivatives from FFPE was assessed by two-way ANOVA. No significant interaction between storage duration and cancer tumor tissue type on the mean absorbance ratio (A 260/ 280 ) of DNA, RNA and miRNA were observed. A significant interaction by storage duration and cancer tumor type on the mean absorbance value (A 280 ) of protein was observed ( Ã P = 0.01) by two-way ANOVA. The mean absorbance value (A 280 ) for proteins derived from more recently stored FFPE (2002-2013) was greater than protein derived from older FFPE

Fit for purpose statistics
Fitness of DNA derivatives from FFPE. The distribution of DNA by fitness category was determined by absorbance ratio (A 260 / 280 ) and by concentration when stratified by storage duration interval of 11 years and tumor tissue type (Table 2). A Cochran-Mantel-Haenszel (CMH) test showed no significant associations of fitness category with the absorbance values or concentrations of DNA by storage duration in 11 year intervals (P = 0.37, P = 0.80; respectively), or cancer tumor tissue type (P = 0.70, P = 0.32; respectively).  Papillary  11  55  7  35  2  10  6  30  10  50  4  20   Adenocarcinoma  3  15  14  70  3  15  7  39  9  50  2  11   Squamous  14  70  6  30  0  0  10  50  7  35  3  15   Total  28  47  27  45  5  8  23  40  26  45  9  16 Fitness of RNA derivatives from FFPE. The distribution of RNA by fitness category was determined by absorbance ratio (A 260 / 280 ) and concentration when stratified by storage duration intervals of 11 years and cancer tumor tissue type (Table 3). Storage duration group was associated with RNA fitness based on absorbance values (CMH test, Ã P = 0.05). No association was observed between cancer tumor tissue type and fitness based on absorbance (CMH test, P = 0.14). A Cochran-Mantel-Haenszel (CMH) test showed no significant associations of fitness category with the concentration values of RNA by storage duration in 11 year intervals or cancer tumor tissue type (P = 0.25, P = 0.13; respectively). The RIN fitness of RNA was somewhat associated with storage duration or cancer tumor tissue type (P = 0.08, P = 0.07; respectively).
Fitness of miRNA derivatives from FFPE. The fitness of miRNA absorbance ratios (A 260 / 280 ) were not associated with FFPE storage duration or tumor tissue type (CMH test, P = 0.29, P = 0.25, respectively) ( Table 4). The concentration fitness of miRNA derived from FFPE was not associated with storage duration or tumor tissue type (CMH test, P = 0.18, P = 0.32; respectively).
Fitness of protein derivatives from FFPE. The fitness of proteins derived from FFPE by was not observd to be related to storage duration but was related to tumor tissue type (CMH test, P = 0.22, Ã P = 0.05; respectively) ( Table 5).
Overall fitness of FFPE blocks. The overall fitness of FFPE blocks, based on the combined assessment of derivatives co-extracted from each block, was not associated with storage duration (CMH test, P = 0.26) ( Table 6). When analyzed by cancer tissue type, adenocarcinoma FFPE tissue bocks had significantly better overall block fitness than either papillary or squamous FFPE blocks (CMH test, Ã P = 0.01).
Dependence of derivative fitness when co-extracted from the same FFPE block. The fitness of one type of derivative extracted from an FFPE block was weakly associated with the fitness of other derivatives co-extracted from the same FFPE block when analyzed by pair-wise Spearman rank correlation matrix (Table 7). In other words, the quality of one derivative in a block did predict the quality of another derivative from the same block. Table 6. The fitness from formalin-fixed paraffin-embedded (FFPE) blocks tissue stratified by the storage duration (11 year intervals) and three tumor tissue types (n = 118) as categorized in Table 1.

Discussion
While previous studies have reported on the fitness of individual nucleic acids or protein extracted from FFPE tissue blocks for genomic and proteomic analyses [1,2,[5][6][7][8][14][15][16][17][18][19][20], the current study is unique in its systematic assessment of the fitness of nucleic acids and protein co-extracted from the same FFPE block. A critical tool in the current study was stratified random sampling. While this method is common in population-based studies, it is seldom used for quality assurance studies in biorepositories, despite the fact that biorepositories are particularly amenable to stratified random sampling for two reasons: (1) the attributes of each sampling unit (e.g., the FFPE block) are readily available (e.g., tumor tissue type, duration of storage, etc.) and (2) the numbers of sampling units falling into each strata are known and do not need to be estimated. This sampling method enabled us to stratify the FFPE blocks on two factors that we hypothesized would most affect derivative fitness: (1) storage duration and (2) tumor tissue type. With equal numbers of FFPE blocks per strata (i.e., a "balanced study design"), the study was powered to detect robust differences in FFPE derivatives based on these two critical factors. Moreover, nucleic acids and proteins in FFPE blocks are extremely sensitive to pre-analytical factors, such as the time between tissue acquisition and fixation, tissue autolysis, or temperature during the paraffin embedding process [2]. Unfortunately, preanalytical factors are seldom annotated for archived FFPE blocks. Morever, durng the 26 years in which these FFPE were archived, numerous developments in formalin fixation methods have been implemented. As the effects of all these pre-analytical factors could not be determined [21], the random stratified sampling used in this study diminished the potential confounding and bias from different pre-analytical factors in the preparation of FFPE tissue. An important innovation of the current study was to combine the fitness levels of the derivatives coextracted from each FFPE block into a single score for the entire block, which enabled assessment of "overall" block fitness. This led to two important observations in the current study: (1) the fitness of one type of nucleic acid or protein derivative did not predict the fitness of another derivative co-extracted from the same FFPE block; e.g., a block with "unfit" RNA may have "fit" miRNA; and (2) the majority of the FFPE blocks had nucleic acids and protein derivatives "fit" for diverse genomic and proteomic purposes ( Table 6). Another finding was that derivatives from adenocarcinoma tissue tended to be more "fit" than derivatives from FFPE containing papillary or squamous cell tumor tissue, an observation which deserves further study.
Some FFPE derivatives survive the extraction and purification better than others. As expected, long-stranded RNA were very labile, especially when RIN was taken into account. Numerous explanations have been proposed to explain the modifications that RNA undergoes as a result of formalin fixation, with the principal change due to the addition of a hydroxymethyl group (methylol) to the nucleic acid backbones, which facilitates the formation of Fitness of derivatives from archived formalin fixed paraffin embedded tissue blocks methylene bridges between amino groups within the nucleic acid chain (see [4] for an excellent review of this process). In addition, formaldehyde facilitates depurination (i.e., loss of the purine base while leaving the DNA backbone intact) of RNA as well as hydrolysis of phosphodiester bonds, leading to the short chains of RNA extracted from FFPE tissue (see also [14,19]). However, it should be emphasized that even though RNA derivatives from FFPE tissue were degraded, which limits their use in many kinds of sequencing analyses, the remaining short strands of RNA remained long enough to be utilized as templates for several genomic methods such as PCR, which uses smaller amplicon sizes (400 base pairs) [18,[22][23][24]. The fitness of miRNA contrasted clearly with the fitness of co-extracted RNA (Table 3). These are non-coding RNAs of approximately 20-22 nucleotides in length, making them less prone to degradation and modification than total RNA due to their already short length and their close association with large protein aggregates [15,16,25,26]. There are several studies that have directly compared miRNA and RNA fitness from paired FFPE blocks and frozen tissue samples, which demonstrated the better fitness of miRNA compared to RNA [15,16,25,26]. In keeping with numerous recent studies of the quantity and quality of DNA extracted from FFPE tissue [20], we observed DNA derivatives to be easier to extract, more stable, and less prone to degradation than RNA. Finally, FFPE blocks yield potentially amplifiable short amplicons (268 base pairs) for nearly all DNA extracted. Importantly, there was no evidence of a difference in the fitness of DNA after long-term storage or one of the three tumor tissue types studied, which is in agreement with several other studies that suggest that the ability to amplify DNA is not diminished with the aging of FFPE blocks [2,21,27,28].
Finally, more than half of the protein derivatives from FFPE were fit for proteomic purposes. The proteins from adenocarcinoma FFPE tissue were especially more "fit" than proteins from squamous cell or papillary FFPE tissue. A significant interaction effect was observed for the absorbance value for proteins, implying that storage duration affected proteins derivatives from tumor tissue types differently. The mean A 280 for proteins derived from recently stored FFPE (2002-2013) was greater than mean A 280 for proteins from older FFPE (1990FFPE ( -2001, with the exception of protein derivatives from adenocarcinoma tissue. There are several limitations to the current study that need to be addressed. The principle limitation is that the samples were not further stratified by the tumor "site" (or organ), which could be a critical determinant of the fitness of the derivatives extracted from the FFPE. However, if the samples were further stratified by tumor site (e.g., skin, lung, liver, etc.), then the number of FFPE blocks in each sample strata would have been small, lowering the statistical power of study. Two technical issues also limit the current study. While the derivatives often fell unambiguously into the fitness categories of "unfit, "fit", and "above fit", which were based on A 260/280 ratio for nucleic acids and the A 280 for proteins, a sample with a borderline absorbance (e.g. an miRNA derivative with a A 260/280 ratio at 1.4996) was difficult to place into a fitness category; i.e., the fitness categories lose their precision along the border ranges. Finally, protein concentration was determined only by A 280 without the benefit of an extinction coefficient or by more advanced methodologies such as amino acid analysis (AAA) or nitrogen content. While quantifying protein in this study by A 280 was convenient and does not consume large amounts of material, proteins have widely varying absorption characteristics due to amino acid content. As such, there is some error in this reading, especially as these were protein mixtures: i.e., non-protein components in the solution may also absorb ultraviolet light in addition to the heterogeneous mixture of proteins present. However, the extinction coefficient can only be calculated accurately when the protein sequence is known and then requires further confirmation through other analyses, such amino acid analysis or light scattering detection and refractive index measurement, all of which were beyond the scope this study.
In summary, we present a unique approach for quality control of derivatives from FFPE that differs from previous studies [6][7][8]) as follows: (a) the fitness of FFPE tissue derivatives can be classified into readily applicable categories for downstream application by bench researchers; (b) multiple types of nucleic acids and protein derivatives can be co-extracted from the same FFPE block, enabling the scoring of overall FFPE block quality; and (c) sampling and statistical methods can be used that enabled reliable estimates of derivative fitness, despite the unknown variation in pre-analytical processing of FFPE blocks. However, much like these previous studies [6][7][8], the current study shows that FFPE blocks remain "a valuable and underexploited resource" [6] for contemporary genomic and proteomic research.