Abstract

BackgroundSingle-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood.ResultsWe use low temperature (6 °C) protease and collagenase (37 °C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNA-seq dataset comprising 155,165 cells from patient cancer tissues, patient-derived breast cancer xenografts, and cancer cell lines. We observe substantial variation in standard quality control metrics of cell viability across conditions and tissues. From the contrast between tissue protease dissociation at 37 °C or 6 °C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37 °C), which are minimized by dissociation with a cold active protease (6 °C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues.ConclusionsThe method and conditions of tumor dissociation influence cell yield and transcriptome state and are both tissue- and cell-type dependent. Interpretation of stress pathway expression differences in cancer single-cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with the identification of such effects in dissociated scRNA-seq experiments.

Highlights

  • Recent advancements in sequencing technologies have allowed for RNA sequencing at single-cell resolution, which can be used to interrogate features of tumor tissues that may not be resolved by bulk sequencing, such as intratumoral heterogeneity, microenvironmental architecture, clonal dynamics, and the mapping of known and de novo cell types

  • Single-cell RNA sequencing of 155,165 cells To uncover transcriptional variation and responses to dissociation method, we generated scRNA-seq data for 155,165 single cells across a range of substrates, cancer types, dissociation temperatures, and tissue states (Fig. 1), using the 10x Genomics Chromium v3 platform [13]. scRNA-seq was performed on cells from patient samples, patientderived breast cancer xenografts (PDXs), and cell lines across ovarian, lymphoid cell, and breast cancers, including fresh and viably frozen samples dissociated at 37 °C or 6 °C and cells incubated at 6 °C, 24 °C, 37 °C, or 42 °C (Fig. 1)

  • We began by examining a set of commonly used quality control (QC) metrics across all 48 sequencing experiments (Fig. 1c), including the total number of genes detected, percentage of transcripts mapping to the mitochondrial genome, and total number of unique molecular identifiers (UMIs) sequenced

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Summary

Introduction

Recent advancements in sequencing technologies have allowed for RNA sequencing at single-cell resolution, which can be used to interrogate features of tumor tissues that may not be resolved by bulk sequencing, such as intratumoral heterogeneity, microenvironmental architecture, clonal dynamics, and the mapping of known and de novo cell types. Due to the sensitivity of single-cell RNA sequencing (scRNA-seq), small changes in gene expression can dramatically influence the interpretation of biological data. The transcriptional behavior of single cells can deviate profoundly from the population as a whole, and gene expression pulse patterns have been shown to contribute significant noise levels to scRNA-seq data [3]. It has been shown that a serine protease (subtilisin A) isolated from a Himalayan glacierresident bacterium, Bacillus lichenformis, is suitable for dissociation of non-malignant renal tissues at 4–6 °C and can reduce scRNA-seq artifacts in these tissues, including reducing global and single-cell gene expression changes [6]. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. The sources of technical and biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood

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