Single-nucleus RNA-seq of frozen archival primary pancreatic ductal adenocarcinoma uncovers multi-compartment intratumoral heterogeneity associated with neoadjuvant treatment.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 4633-4633
Author(s):  
William L. Hwang ◽  
Karthik Jagadeesh ◽  
Jimmy Guo ◽  
Hannah I. Hoffman ◽  
Orr Ashenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Neoadjuvant Treatment ◽  
Intratumoral Heterogeneity ◽  
Ductal Adenocarcinoma ◽  
Type I ◽  
Malignant Cells ◽  
Rna Seq ◽  
Single Nucleus

4633 Background: Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease and existing molecular subtypes do not inform clinical decisions. Previously identified bulk transcriptomic subtypes of PDAC were often unintentionally driven by “contaminating” stroma. RNA extraction from pancreatic tissue is difficult and prior single-cell RNA-seq efforts have been limited by suboptimal dissociation/RNA quality and poor performance in the setting of neoadjuvant treatment. We developed a robust single-nucleus RNA-seq (sNuc-seq) technique compatible with frozen archival PDAC specimens. Methods: Single nuclei suspensions were extracted from frozen primary PDAC specimens (n = 27) derived from patients with (borderline)-resectable PDAC who underwent surgical resection with or without neoadjuvant chemoradiotherapy (CRT). Approximately 170,000 nuclei were processed with the 10x Genomics Single Cell 3’ v3 pipeline and gene expression libraries were sequenced (Illumina HiSeq X). Results: Distinct nuclei clusters with gene expression profiles/inferred copy number variation analysis consistent with neoplastic, acinar, ductal, fibroblast, endothelial, endocrine, lymphocyte, and myeloid populations were identified with proportions similar to corresponding multiplexed ion beam imaging. Non-negative matrix factorization revealed intra-tumoral heterogeneity shared across patients. Neoplastic cells featured eight distinct transcriptional topics characterized by developmental (epithelial, mesenchymal, endoderm progenitor, neural progenitor) and environmental (anabolic, catabolic, cycling, hypoxic) programs. CAFs exhibited four different transcriptional topics (activated/desmoplastic, myofibroblast, neurogenic, osteochondral). Differential gene expression and gene set enrichment analyses demonstrated that CRT was associated with an enrichment in myogenic programs in CAFs, activation pathways in immune cells, and type I/II interferons in malignant cells. CRT was also associated with a depletion in developmental programs within malignant cells. Conclusions: We uncovered significant intratumoral heterogeneity and treatment-associated differences in the malignant, fibroblast, and immune compartments of PDAC using sNuc-seq. Deconvolution of clinically-annotated bulk RNA-seq cohorts and characterization of intercellular interactions with receptor-ligand analysis and spatial transcriptomics are ongoing.

scholarly journals Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment

2020 ◽  
Author(s):  
William L. Hwang ◽  
Karthik A. Jagadeesh ◽  
Jimmy A. Guo ◽  
Hannah I. Hoffman ◽  
Payman Yadollahpour ◽  
...  
Keyword(s):  
Molecular Taxonomy ◽  
Neoadjuvant Treatment ◽  
Cell Types ◽  
Malignant Cell ◽  
Ductal Adenocarcinoma ◽  
Precision Oncology ◽  
Malignant Cells ◽  
Rna Seq ◽  
Cytotoxic Treatment ◽  
Single Nucleus

ABSTRACTPancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease. Characterizing PDAC by mRNA profiling remains particularly challenging. Previously identified bulk expression subtypes were influenced by contaminating stroma and have not yet informed clinical management, whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for frozen archival PDAC specimens and used it to study both untreated tumors and those that received neoadjuvant chemotherapy and radiotherapy (CRT). Gene expression programs learned across untreated malignant cell and fibroblast profiles uncovered a clinically relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Moreover, in the increasingly-adopted neoadjuvant treatment context, there was a depletion of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states, which may be more resilient to cytotoxic treatment. Spatially-resolved transcriptomics revealed an association between malignant cells expressing these basal-like programs and higher immune infiltration with increased lymphocytic content, whereas those exhibiting classical-like programs were linked to sparser macrophage-predominant microniches, perhaps pointing to susceptibility to distinct therapeutic strategies. Our refined molecular taxonomy and spatial resolution can help advance precision oncology in PDAC through informative stratification in clinical trials and insights into differential therapeutic targeting leveraging the immune system.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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