The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n = 18) and a solid tumor type, ovarian cancer (OC, n = 18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell type identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n = 6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.

Cancer can recur when a subset of tumor cells, termed persister cells, survive therapy and re-enter the cell cycle. Through single-nucleus multi-omic profiling (single-nucleus RNA sequencing [snRNA-seq] and single-nucleus assay for transposase-accessible chromatin by sequencing [snATAC-seq]) of (1) non-malignant fallopian tubes and (2) treatment-naive and (3) neoadjuvant-chemotherapy-treated samples from patients with high-grade serous ovarian carcinoma (HGSOC), we identify a persister cell signature defining the chemotherapy-tolerant state. The chromatin features of the signature are detectable in residual tumors after treatment and in treatment-naive tumors from patients who later develop resistance. Further, the signature independently predicts chemotherapy response in metastatic HGSOC and patient-derived xenograft models. Cells enriched in the persister state display a subset of genes primed for expression before treatment, an altered cell cycle, and stress-response programs associated with poor clinical outcomes. These findings suggest that an intrinsic regulatory program primes tumor cells toward chemotherapy tolerance and reveal new vulnerabilities that can be targeted with chromatin-modifying agents to prevent cancer recurrence.

Cancer can recur when a subset of tumor cells, denoted here as persister cells, are able to survive therapy and re-enter the cell cycle. The precise mechanisms that confer the persister state and whether it is characteristic of a subgroup of cells or arises from multiple cellular lineages remain poorly understood. We hypothesize that an epigenetic signature underlies the drug-tolerant persister state, characterized by transcriptional and chromatin accessibility changes that promote survival of residual cancer following chemotherapy. To identify clinically relevant features of persister cells in untreated tumors and residual disease, we performed single-cell multiomic profiling (snRNA+snATAC) on a cohort of non-malignant fallopian tube, treatment-naïve, and neoadjuvant chemotherapy (NACT)-treated high-grade serous ovarian cancer (HGSOC) samples. We identified differences in gene expression and open chromatin between naïve and residual patient tumors following chemotherapy. Although only a small proportion of the differentially expressed genes enriched in residual HGSOC overlapped with established gene sets for chemo-response and patient prognosis, the epigenomic analysis revealed activity of several DNA-binding factors that are both enriched upon chemotherapy and also high in resistant tumors prior to treatment. From this analysis, we identified an epigenetic signature that precedes expression and defines the persister state. This epigenetic signature also correlated with chemotherapy sensitivity and resistance using patient-derived xenograft models of HGSOC. Gene regulatory networks driven by the persister signature are involved in the activation of oncogenic pathways, including changes to the cell cycle promoting quiescence and stress response. Further study of the persister cells identified by this epigenetic signature may increase understanding of the mechanisms underlying persister cell survival and reveal new vulnerabilities that could be exploited to delay or prevent cancer recurrence.

The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n=18) and a solid tumor type, ovarian cancer (OC) (n=18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n=6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.