Dissociation protocols used for sarcoma tissues bias the transcriptome observed in single-cell and single-nucleus RNA sequencing

All experiments were conducted per protocols and conditions approved by the University of Texas MD Anderson Cancer Center (MDACC; Houston, TX) Institutional Animal Care and Use Committee (eACUF Protocols #00000712-RN02). Male NOD (SCID)-IL-2Rg^null mice (The Jackson Laboratory; Farmington, CT) were subcutaneously injected with PDX explants (2 mm) to generate xenografts. All mice were maintained under barrier conditions and treated using protocols approved by The University of Texas MD Anderson Cancer Center’s Institutional Animal Care and Use Committee. SA98 (full id: MDA-SA98-TIS02), OS1, and OS31, are PDX lines maintained by the Pediatric Solid Tumors Comprehensive Data Resource Core [13]. DSRCT and ES PDX lines were generated from the Sarcoma Tissue Bank at MD Anderson Cancer Center and maintained by the Ludwig lab. Once their tumors reached a volume of 150 mm [3], tumors were explanted and a portion was flash-frozen for snRNA-seq, while the remainder underwent dissociation.

Samples were collected and immediately placed into MACS® Tissue Storage Solution (Miltenyi Biotec) and kept on ice during transport. On arrival to the laboratory, samples were minced using a scalpel into fragments < 4 mm under aseptic conditions. Next, samples were evenly split for either warm or cold enzymatic dissociation.

For warm dissociation of ES and DSRCT PDX specimens, the human Tumor Dissociation Kit (Miltenyi Biotec) was used. The dissociation was performed under manufacturer’s protocol using the gentleMACS™ Dissociator (Miltenyi Biotec), a benchtop instrument for the semi-automated dissociation of tissues into single-cell suspensions. The gentleMACS Program sequenced followed the suggestion for ‘Soft’ Tumor type. Briefly, tissue pieces were placed in the gentleMACS™ C Tubes containing the enzyme mix. The gentleMACS™ C Tubes were then placed onto the gentleMACS™ Dissociator, and the program ‘h_tumor_01’ was run, followed by a 30-min incubation at 37 °C with rotation using the MACSmix™ Tube Rotator. Afterward, we placed the gentleMACS™ C Tube onto the gentleMACS™ Dissociator and ran the ‘h_tumor_02’ program. This was followed by a 30-min incubation at 37 °C with rotation. Finally, we placed the gentleMACS™ C Tube onto the gentleMACS™ Dissociator and ran the ‘h_tumor_03’ program. Following completion of the program, 2 × volume of media was added to the samples. This was followed by filtration through a MACS SmartStrainer (70 μm, Miltenyi Biotec) and centrifugation at 300 g for 5 min. Cells were resuspended in 90% FBS and 10% DMSO at a concentration of 1 million cells per mL and placed in a Thermo Scientific™ Mr. Frosty™ Freezing Container in a -80 °C freezer.

For warm dissociation of OS PDX specimens, tissue was minced into < 4 mm pieces with a sterile scalpel or scissors. The tissues were washed several times with Hank’s Balanced Salt Solution (HBSS). HBSS was next aspirated, and dissociation buffer (HBSS, 1 mg/mL collagenase, 3 mM CaCl₂, 1 μg/mL DNase) was added to submerge the tissue. The tissue is then incubated at 37 °C for up to 12 h. The cell suspension was then filtered using a 40 μm cell strainer. The filtrate is pelleted using centrifugation at 400 g for 5 min. Cells were resuspended freezing medium and placed in a Thermo Scientific™ Mr. Frosty™ Freezing Container in a -80 °C freezer.

For cold dissociation, the protocol was adapted from Adam et al. [5]. Cold protease solution was prepared from 5 mM CaCl₂, 10 mg/mL B. Licheniformis protease, and 125 U/mL DNase I in 1 × PBS. Tissue was minced using a scalpel into fragments under 0.5 mm. Pieces were placed in a MACS C-tube, and 5 mL of ice-cold cold protease solution was added. The samples were incubated for 10 min at 4 °C with rocking. This was followed by placing the pieces in a gentleMACS™ Dissociator (Miltenyi Biotec) and running the m_brain_03 program twice. Afterward, the samples were centrifuged at 300 g for 5 min and resuspended in 3 mL of trypsin–EDTA for 1 min at room temperature. The trypsin–EDTA was then neutralized using ice-cold 10% FBS in 1 × PBS and triturated. This was followed by filtration through a MACS SmartStrainer (70 μm, Miltenyi Biotec) and centrifugation at 300 g for 5 min. Cells were resuspended freezing medium at a concentration of 1 million cells per mL and placed in a Thermo Scientific™ Mr. Frosty™ Freezing Container in a -80 °C freezer. Cryovials were moved to LN2 storage for the long-term.

The cells were removed from the LN₂ or -80 °C freezer, if they were recently cryopreserved and placed into a 37 °C water bath for 3 min. The contents were then transferred to a 15 mL centrifuge tube. 1 mL of complete medium was used to wash the cryovial and added dropwise into the centrifuge tube. Next, 8 mL of complete medium was added dropwise to reduce osmotic shock. Cells were then centrifuged at 300 g for 5 min and resuspended in 1 × PBS supplemented with 0.04% BSA. This was followed by live cell enrichment using FACS. Single-cell suspensions were stained with Calcein AM live cell stain and SYTOX™ Red dead cell stain.

The protocol was adapted from Habib et al. [9]. We isolated nuclei from fresh-frozen tissue using the Nuclei EZ Prep Kit (Sigma-Aldrich). Fresh-frozen tissue specimens were cut into pieces < 5 mm over dry ice and then placed in 0.5 mL ice-cold EZ lysis buffer. This was followed by homogenizing using a Chemglass Life Sciences Supplier BioVortexer Mixer (Fisher Scientific) attached with a plastic microcentrifuge pestle on ice. Then 1 mL of ice-cold EZ lysis buffer was added, and samples were incubated on ice for 5 min. Debris was filtered out using a pluriStrainer Mini 70 μm into a new tube. This was followed by centrifugation at 500 g for 5 min. Samples were then incubated with 1 mL of ice-cold EZ lysis buffer on ice for 5 min, followed by centrifugation. Afterward, the supernatant was aspirated, and 0.5 mL of Nuclei Wash and Resuspension Buffer (NWRB, 1X PBS supplemented with 1.0% BSA and 0.2U/μl RNase Inhibitor) was carefully added without disrupting the pellet, which was followed by 5 min of incubation. Next, we added 0.5 mL of NWRB and centrifuged at 500 g for 5 min. We repeated the wash and incubation once more, followed by centrifugation. The supernatant was aspirated, and the nuclei were resuspended in NWRB. A portion was visualized with Trypan blue under the microscope to inspect for debris and nuclei integrity.

To sort nuclei, single-nucleus suspensions were stained with 7-AAD in NWRB for 5 min on ice. Then a BD cell sorter was used to sort up to 100,000 7-AAD positive events. Quality control of post-sort nuclei concentration was evaluated under a microscope to ensure adequate count. This was followed by loading nuclei onto a 10x chip.

We followed the standard protocol set by 10x  Genomics for single-cell/single-nucleus capture. A targeted capture of 5000 single cell or single nucleus were loaded onto each channel of a Chromium single-cell 3’ Chip. The single cells and single nuclei were partitioned using the gel beads within the Chromium Controller. Afterward, we performed cDNA amplification and fragmentation. This was followed by index and adapter attachments. Samples were pooled and sequenced on a NovaSeq 6000 with targeted sequence depth at 100,000 reads/cell or nucleus.

We used Cell Ranger mkfastq to generate demultiplexed FASTQ files. Reads were aligned to the human GRCh38 genome, and reads were then quantified as UMIs by Cell Ranger count. For snRNA-seq, reads were mapped with both introns and exons in Cell Ranger 5.0 using the include-introns option for counting intronic reads [10].

We performed QC and normalization separately for each sarcoma PDX. We followed the guidelines for QC from OSCA and others [14]. We inspected UMIs, gene counts, and the percentage of mitochondrial genes and identified outliers based on median absolute deviation (MAD). We used a strict value of 2 or more MADs from the median while also using generic cut-offs. Cells that did not meet the criteria were removed from the analysis. Scrublet was used to predict and detect doublets within the data [15]. While doublets were flagged, there was not a single cluster of doublets, which would be evident as an artifact, so no cells were removed. The number of cells analyzed pre- and post-quality control are listed in Table S1.

Seurat v3 was used for sample normalization, dimensional reduction, scaling, and differential expression analysis [16]. We used the Wilcoxon test to compare gene expression between protocols. Enrichr was used for pathway enrichment. We set a log2 fold change threshold of log2(1.5) or greater. This will result in genes that are 50% greater than the baseline. The AddModuleScore function in Seurat v3 was used to observe the averaged gene expression of the different gene sets. We used curated gene sets of a warm dissociation signature from O’Flanagan et al. [4], EWS-FLI1 gene targets [17], EWS-WT1 gene targets [18] and osteoblastic and chondroblastic signatures classically associated with the tissue origin of OS (Table S2). The osteoblastic and chondroblastic signatures were found on Harmonizome (https://​maayanlab.​cloud/​Harmonizome/​). The osteoblastic signature was specifically found in the GeneRIF Biological Term Annotations under ‘Osteoblastic’. The chondroblastic signature was specifically found in the TISSUES Text-mining Tissue Protein Expression Evidence Scores under ‘Chondroblasts’. To find conserved markers between dissociation methods, we used the function FindConservedMarkers in Seurat v3. We performed integration using the integration functions within Seurat v3. The datasets were integrated by dissociation protocol.

To classify nuclei and cells using the length bias and warm dissociation scores, data sets were randomly split into a training and test set. To prevent data leakage, scaled data was not used. We then calculated the gene set scores separately on the training and test sets. A logistic regression model was fit to the training set on either the warm dissociation or length bias score to predict for cells and nuclei, respectively. We calculated the probabilities and the area under the curve using the pROC v1.18.0 package. This was compared to a random gene signature equal in the number of genes of either length bias or warm dissociation gene sets.

Results reported as boxplots display the data distributions (centerline: median, box limits: first and third quartiles, and the whiskers are the highest and lowest values that are no greater and smaller than 1.5 × the interquartile range) as specified in the Figure Legends. Numerical values are reported as mean ± SEM. One-way ANOVA and the Wilcoxon rank-sum test were performed using the R packages ggpubr and stat. A p-value of less than 0.05 was considered statistically significant.

In this work, we studied sarcomas from varying tissue origins, including osteosarcoma (OS), Ewing sarcoma (ES), and desmoplastic small round cell tumor (DSRCT) (Fig. 1). We used different dissociation protocols: Miltenyi Tumor Dissociation Kit, cold-active protease derived from Bacillus licheniformis, and Nuclei EZ Prep. These three protocols are described herein as Warm, Cold, and Nuclei protocols. For the OS specimens, we used the same Nuclei protocol and a different Warm protocol optimized for OS. For DSRCT and ES specimens, we performed the additional Cold protocol, using the cold-active protease, as we had more specimens available. Each sarcoma subtype included three PDX specimens derived from different patients. In total, we analyzed 125,831 whole-cells and nuclei across the three sarcoma subtypes and three dissociation protocols.

Previous work has shown that dying and dead cells can influence the transcriptome and introduce artifacts that preclude useful biological insight [4]. To evaluate this effect in sarcoma, we evaluated and compared several protocols based on cell/nucleus quality and transcriptomic signatures. For cell/nucleus quality, we measured the percent of reads mapping to the transcriptome, number of genes, unique molecular identifiers (UMIs), and percent of mitochondrial genes for each cell or nucleus. We optimized our strategy to enrich for live single cells and single nuclei, respectively, by incorporating fluorescently activated cell sorting (FACS) or fluorescently activated nuclei sorting (FANS) prior to sequencing (Fig. S1).

Next, we evaluated common quality control (QC) metrics across all samples to assess the effect of each dissociation protocol (Fig. 2A). We observed some variations in QC metrics for the number of genes and UMIs when comparing between protocols while limiting the comparisons to between PDXs of each sarcoma subtype (Fig. 2B-D). However, some of the variations could be explained by the number of cells sequenced and sequencing depth since there is an inverse relationship between these two metrics when total reads are kept constant. Expectedly, nucleus samples demonstrated little to no percentage of mitochondrial genes since purified nuclei do not contain mitochondrial transcripts, which were enriched in the whole-cell samples. With respect to each protocol, we did not discern a positive or negative influence on the QC metrics.

To determine whether protocol-specific differences in gene expression exist, we visualized the UMAP embeddings of all whole-cells and nuclei without batch or technical corrections. When colored by sarcoma subtype, the same sarcoma subtype cluster together but with two distinct clusters for each sarcoma subtype except for OS (Fig. 3A). We suspect that this may be due to biases from the different dissociation protocols. When labeled by fresh specimens (whole cells) or frozen specimens (nuclei), we identified a distinct delineation between fresh and frozen tissues in the UMAP. The observed differences within the UMAP, we hypothesized, stem from biological artifacts linked to fresh tissue dissociation or technical artifacts that reflect a core set of mRNA transcripts preferentially retained within the nucleus. By coloring the UMAP embedding by dissociation type, cells processed using the Warm and Cold methods sometimes overlapped for each PDX (e.g., ES-1 and ES-4), whereas Nuclei clusters remained segregated.

Previous reports using normal epithelial tissues and carcinomas revealed that warm enzymatic dissociation (i.e., at 37 °C) invoked a distinct ‘Warm Dissociation Signature’ enriched in FOS, FOSB, and JUN [7]. To investigate if similar dissociation-specific biases occur in sarcomas exposed to collagenase at 37 °C, we selected a partial list of the top genes within the Warm Dissociation signature and compared their expression. Averaged gene expression from each sarcoma subtype showed that these genes are, indeed, elevated in the Warm protocol (Fig. 3B). Furthermore, since prior literature has stated that long non-coding RNAs (lncRNAs) are localized to the nucleus, we also explored their gene expression in these specimens [19]. Consistent with previous results, we observed that lncRNAs were elevated in the Nuclei protocol. Together, these results indicate that the method chosen for dissociation has a profound effect on gene expression.

To further characterize how scRNA-seq and snRNA-seq affect transcript abundance, we analyzed differentially expressed genes (DEGs). Warm and Nuclei protocols demonstrated a consistent trend for each sarcoma type. Genes with the largest fold-change in the Warm method included mitochondrial and ribosomal protein genes (Fig. 4, Tables S3-S5). This was expected since the mitochondria (and their innate transcripts) are removed entirely during the Nuclei dissociation method. Similarly, enrichment of ribosomal protein genes was also noted in a comparison between scRNA-seq and snRNA-seq for kidney tissue [20]. On the other hand, genes enriched in the Nuclei protocol did not have a clear consensus or overlap between sarcoma types. In addition, we found a common set of 325 genes enriched after filtering for log2 fold change over 1.5. Similarly, we found 117 genes enriched in the Nuclei protocol (Fig. S2A, B). Next, we performed a pathway enrichment of the MSigDB hallmark gene set using Enrichr. We observed stress-associated pathways in each sarcoma type that was enriched in the Warm protocol, including Hypoxia, Apoptosis, DNA repair, and TNF-alpha Signaling via NF-kB, which is consistent with prior work [4]. Interestingly, we noted that Epithelial-Mesenchymal Transition (EMT) was significantly enriched in all sarcoma subtypes, which was not noted in prior works with normal or carcinoma samples [4]. For the Nuclei protocol, we observed enrichment in Mitotic Spindle. When comparing the Warm and Cold protocols for only ES and DSRCT, we again observed an increase in several of the commonly identified stress-related pathways like previous results (Fig. S3, Tables S6 and S7). The UMAP embedding suggested that the differences in Warm and Cold are minimal due to the two data sets overlapping for all PDX but ES-6 and DSRCT-4, which only partially overlapped (Fig. 3A). Furthermore, we found in total 14 commonly enriched pathways suggesting a core set of conserved genes enriched in the Warm protocol for sarcoma samples when compared against the Nuclei protocol (Fig. S2C). Interestingly, we did not observe any common pathways enriched between sarcomas for the Nuclei protocol.

Next, to evaluate if any of the protocols influenced signatures associated with a particular sarcoma type, we analyzed the expression of gene sets curated from the literature (Table S2). For ES, we used a set of genes that are direct targets of the EWS-FLI1 fusion protein, which included KDSR, CAV1, and FCGRT [17]. Likewise, a gene set for EWS-WT1 targets, generated from cell lines, was used to evaluate the effect of each protocol in DSRCT [18]. Since OS lacks a clearly defined gene set and often contains cells of partial fibroblastic, chondroblastic, or osteoblastic lineage commitment, we utilized curated genes associated with osteoblastic and chondroblastic signatures classically associated with the putative tissue origin of OS. Strikingly, the unique sarcoma subtype-specific gene signatures were preserved across all dissociation protocols (Fig. 5). This suggests that regardless of dissociation protocol biases, the cells still exhibit the classic signatures for each sarcoma studied. For instance, the EWS-WT1 gene targets are upregulated in only the DSRCT PDX specimens. Likewise, the EWS-FLI1 target genes are only enriched in ES, irrespective of the protocol used. However, when comparing dissociation protocols for ES only, we observed overexpression of the EWS-FLI1 gene set specifically in the Nuclei protocol. This suggested that for ES only, there may be a Nuclei protocol bias influencing the EWS-FLI1 target gene expression. While we did not observe this phenomenon in the other gene sets, we explored the idea of a Nuclei protocol bias.

Subsequent analysis revealed that several enriched genes in the Nuclei protocol are coded by transcripts longer in length compared to those enriched in the Warm protocol. To further investigate this interesting finding, we compared the gene lengths of commonly enriched genes in the Warm versus Nuclei protocol for all sarcomas. We found that genes enriched in the Nuclei protocol had significantly longer genes (Wilcoxon test, p-value < 2.2e-16) (Fig. S4A).

This suggests that there is a possible gene length-associated bias in snRNA-seq. Recent work indicated that hybridization of the polyT RT-primer to intronic polyA stretches of nascent transcripts results in the gene length bias [21]. Across the different sarcoma subtypes, we observed higher exonic reads for the Warm protocol (DSRCT: 39.8 ± 2.7%, ES: 42.1 ± 12.0%, OS: 45.7 ± 1.9%) versus the Nuclei Protocol (DSRCT: 14.3 ± 0.9%, ES: 11.8 ± 3.2%, OS: 16.1 ± 1.1%) (Fig. 6A). Conversely, there were higher intronic reads for the Nuclei protocol (DSRCT: 55.4 ± 3.2%, ES: 48.2 ± 13.1%, OS: 52.3 ± 5.6%) versus the Warm Protocol (DSRCT: 26.4 ± 3.2%, ES: 17.2 ± 7.2%, OS: 27.7 ± 1.6%). Regardless of sarcoma subtype, our analysis showed that 52.0 ± 7.5% of reads for Nuclei mapped to intronic regions whereas 24.0 ± 4.9% of reads were mapped for Warm protocol (Fig. 6B). Post hoc comparison with Bonferroni correction between Warm and Cold against Nuclei was significantly different for both Exonic (p < 2.9e-13) and Intronic (p < 4.4e-12) regions but not for the intergenic region. We binned the genes into quartiles based on the gene length termed as Short (0–8077 nt), Short Med. (8078–24,399 nt), Long. Med. (24,400–66,502 nt), and Long (> 66,502 nt). On average, 55.6% of total genes greater than 66,502 nt (Long) were enriched in the Nuclei protocol compared to 28.2% and 26.6% in the Warm and Cold protocols, respectively (Fig. 6C). Interestingly, we also observed an opposite effect in the short genes (0–8077 nt) with 20.7% and 21.4% of the total genes in Warm and Cold protocols respectively as opposed to 4.1% in the Nuclei protocol (Fig. 6C).

To answer if there was a bias due to polyA stretches, we counted the number of polyA regions, defined as greater than 15 A repeats, within the full-length cDNA, including both intronic and exonic regions for every gene. We observed a significant correlation between increasing gene length and polyA regions (R = 0.7, p-value < 2.2e16, (Fig. 6D). While we saw a positive correlation between scRNA and snRNA expression for each sarcoma type, there is a skew toward higher expression of genes that are longer and containing many polyA regions for snRNA data (Fig. S4B-D).

To evaluate the enrichment of long transcripts, we generated a length bias score by taking the top 200 genes with the highest number of polyA regions and combining them into a signature gene set (Table S8). Note that these genes were chosen purely by length and are agnostic to the underlying biology. In addition, we evaluated the expression of a previously generated warm dissociation signature from O’Flanagan et al. [4] Our results demonstrated that the warm dissociation signature is associated with both the Warm and Cold protocols (Fig. 6E). Comparisons showed a significant difference when comparing either Warm versus Nuclei or Cold versus Nuclei. While the stress-response genes in the warm dissociation signature are minimized in the Cold protocol, the warm dissociation signature may still be active in Cold compared to Nuclei. Since cells under Cold dissociation are still alive and functioning, fresh-frozen tissue used in the Nuclei protocol represents a snapshot of the cell’s state in the tissue that can no longer change due to the dissociation method. On the other hand, expression of the length bias score is only observed in samples profiled using snRNA-seq, where we report a significant difference for the Nuclei protocol when compared against the other two protocols. Together, these signatures robustly delineated the biases imparted by scRNA- and snRNA-seq for the different sarcoma subtypes.

To further illustrate this, we evaluated logistic regression models using the length bias and warm dissociation signatures to classify affected cells. We randomly split the ES data set into training and test groups. Using the logistic regression model, we could accurately predict samples that underwent the Nuclei protocol (AUC = 1.00) and whole-cells that displayed stress from the Warm protocol (AUC = 0.92) (Fig. S5). We applied the same model to the OS and DSRCT data set and observed the same findings (Fig. S6). To test if we could extrapolate this classifier to single-cell and single-nucleus libraries processed outside our lab, we used data from a recent paper [6]. In this work, the authors used collagenase type 4 at 37 °C to dissociate a neuroblastoma PDX (O-PDX) into single cells and Tween with salts and Tris to dissociate O-PDX into single nuclei [6]. The authors demonstrated that the same Warm dissociation signature we evaluated was elevated in whole-cells when compared to nuclei. Like our data, we applied the length bias and warm dissociation signatures to the O-PDX data set and could accurately predict samples that were nuclei (AUC = 1). Importantly, we observed the same findings in a neuroblastoma resection from a patient specimen to rule out if this was limited to only PDX specimens. Our results suggest that single nuclei could readily be classified just by the length bias score and that this may not be limited to just PDX samples (Fig. S7). This implies that snRNA-seq enriches for longer transcript when compared to scRNA-seq from paired samples.

We suspected that when comparing sarcoma signatures between protocols for ES, the elevated expression of the EWS-FLI1 gene set in the Nuclei protocol may be due to the gene length-associated bias in snRNA-seq (Fig. 5). For each sarcoma signature, we divided the genes into four bins based on quartiles of gene length (Fig. S8A). As we expected, for the EWS-FLI1 target gene set, over 40% of genes in this specific set were considered long (≥ 66,502 nt). This enrichment was not observed in the other gene sets. To further explore the effect of longer genes, we split the EWS-FLI1 target gene set into two groups – short (< 65,502 nt) and long (≥ 65,502 nt) genes. Next, we evaluated the resulting expression in ES and indeed observed a gene length-associated bias but only in the group that included long genes (Fig. S8B).

As demonstrated by our UMAP embedding for OS (Fig. 7), the same samples processed simultaneously by scRNA-seq and snRNA-seq exhibit large batch effects and vastly different transcriptomic signatures. This complicates downstream analyses – even within the same cancer type – and will present unique challenges when investigators try to apply lessons learned from a dataset assessed by scRNA-seq to another generated in parallel using snRNA-seq.

One could theoretically solve the dilemma using a brute-force approach that runs each sample twice, first by scRNA-seq then again using snRNA-seq, assuming of course that sufficient tissue exists, However, this method is likely to be impractical given tissue scarcity, frequent lack of paired fresh/frozen tissues, and the redundant costs associated with creating and sequencing the cDNA library twice per sample. Prospective collection of fresh tissues for rare cancers like sarcoma, or even rarer sarcoma subtypes like OS, ES, or DSRCT, presents additional hurdles.

To counter batch effects induced by sample handling, inter-operator variation, and differing technologies (e.g., CEL-Seq, Fluidigm, 10x  Chromium), several bioinformatic tools exist to remove covariates. Utilizing the integration workflow for Seurat v3, we corrected for protocol biases and integrated each dataset of matched PDX specimens [16]. Each sarcoma subtype dataset was split by dissociation method and then integrated.

As can be seen after applying integration, technical biases from dissociation protocols are mitigated, and PDX specimens with similar cell states now cluster together (Fig. 7A). As an example, the Warm dissociation signature that was previously enriched in fresh tissues analyzed by scRNA-seq, is now homogeneously distributed and not affecting the clustering of samples (Fig. 7B). Similarly, the length bias score, which was previously causing the samples profiled by snRNA-seq to separate is now mitigated (Fig. 7B).

When observing conserved markers for OS PDXs, we predicted that both fresh and frozen from the same PDX would exhibit the same DEGs when compared to other PDXs. Based on the Fig. 7C, we observed that the DEGs are conserved between fresh and frozen specimens. As we expected, the unintegrated UMAP does not neatly align the conserved markers, which is most likely due to the technical biases that we have shown influencing the algorithm (Fig. 7D, left column). Upon integration of the samples, we can clearly observe the conserved DEGs localizing to each PDX (Fig. 7D, right column). We extended this same analysis to both ES and DSRCT, and we observed the same effects (Figs. S9 and S10). Like batch corrections, this illustrates the need for properly integrating diverse datasets, where in this work, paired specimens underwent different dissociation protocols to reliably perform downstream analyses.