The majority of patients with pancreatic ductal adenocarcinoma (PDA) develop metastatic disease after resection of their primary tumor. We found that livers from patients and mice with PDA harbor ...single disseminated cancer cells (DCCs) lacking expression of cytokeratin 19 (CK19) and major histocompatibility complex class I (MHCI). We created a mouse model to determine how these DCCs develop. Intraportal injection of immunogenic PDA cells into preimmunized mice seeded livers only with single, nonreplicating DCCs that were CK19
and MHCI
The DCCs exhibited an endoplasmic reticulum (ER) stress response but paradoxically lacked both inositol-requiring enzyme 1α activation and expression of the spliced form of transcription factor XBP1 (XBP1s). Inducible expression of XBP1s in DCCs, in combination with T cell depletion, stimulated the outgrowth of macrometastatic lesions that expressed CK19 and MHCI. Thus, unresolved ER stress enables DCCs to escape immunity and establish latent metastases.
Single-cell RNA-seq's (scRNA-seq) unprecedented cellular resolution at a genome-wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that ...average over bulk tissue extracts. However, scRNA-seq data sets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present a
ingle-cell RNA-seq
nalysis and
lustering
valuation (SAKE), a robust method for scRNA-seq analysis that provides quantitative statistical metrics at each step of the analysis pipeline. Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range of cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors (BRAFi). Single-cell RNA-seq data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor-resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resistance. These experimentally validated markers of BRAFi resistance share overlap with previous analyses in different melanoma cell lines, demonstrating the generality of these findings and highlighting the utility of single-cell analysis to elucidate mechanisms of BRAFi resistance.
Many lines of evidence have indicated that both genetic and non-genetic determinants can contribute to intra-tumor heterogeneity and influence cancer outcomes. Among the best described sub-population ...of cancer cells generated by non-genetic mechanisms are cells characterized by a CD44+/CD24- cell surface marker profile. Here, we report that human CD44+/CD24- cancer cells are genetically highly unstable because of intrinsic defects in their DNA-repair capabilities. In fact, in CD44+/CD24- cells, constitutive activation of the TGF-beta axis was both necessary and sufficient to reduce the expression of genes that are crucial in coordinating DNA damage repair mechanisms. Consequently, we observed that cancer cells that reside in a CD44+/CD24- state are characterized by increased accumulation of DNA copy number alterations, greater genetic diversity and improved adaptability to drug treatment. Together, these data suggest that the transition into a CD44+/CD24- cell state can promote intra-tumor genetic heterogeneity, spur tumor evolution and increase tumor fitness.
BackgroundThe tumor microenvironment (TME) is composed of highly heterogeneous extracellular structures and cell types such as endothelial cells, immune cells, and fibroblasts that dynamically ...influence and communicate with each other. The constant interaction between a tumor and its microenvironment plays a critical role in cancer development and progression and can significantly affect a tumor’s response to therapy and capacity for multi-drug resistance. High resolution analyses of gene and protein expression with spatial context can provide deeper insights into the interactions between tumor cells and surrounding cells within the TME, where a better understanding of the underlying biology can improve treatment efficacy and patient outcomes. Here, we demonstrated the ability to perform streamlined multi-omic tumor analyses by utilizing the 10X Genomics Visium Spatial Gene Expression Solution for FFPE with multiplex protein enablement. This technique simultaneously assesses gene and protein expression to elucidate the immunological profile and microenvironment of different breast cancer samples in conjunction with standard pathological methods.MethodsSerial (5 µm) sections of FFPE human breast cancer samples were placed on Visium Gene Expression (GEX) slides. The Visium GEX slides incorporate ~5,000 molecularly barcoded, spatially encoded capture spots onto which tissue sections are placed, stained, and imaged. Following incubation with a human whole transcriptome, probe-based RNA panel and an immuno-oncology oligo-tagged antibody panel, developed with Abcam conjugated antibodies, the tissues are permeabilized and the representative probes are captured. Paired GEX and protein libraries are generated for each section and then sequenced on an Illumina NovaSeq at a depth of ~50,000 reads per spot. Resulting reads from both libraries are aligned and overlaid with H&E-stained tissue images, enabling analysis of both mRNA and protein expression. Additional analyses and data visualizations were performed on the Loupe Browser v4.1 desktop software.ConclusionsSpatial transcriptomics technology complements pathological examination by combining histological assessment with the throughput and deep biological insight of highly-multiplexed protein detection and RNA-seq. Taken together, our work demonstrated that Visium Spatial technology provides a spatially-resolved, multi-analyte view of the tumor microenvironment, where a greater understanding of cellular behavior in and around tumors can help drive discovery of new biomarkers and therapeutic targets.
Background
Despite years of studies and effort, the best strategies for treating prostate cancer and minimizing the complications of treatment remain unanswered questions. This gap in knowledge is ...partially due to the inability to dissect the complex heterogeneous tumor microenvironment (TME) and immune compartment. Spatially resolved molecular profiling of tumor sections will enhance our understanding of these complexities; However, it has been particularly challenging to do spatial molecular profiling in formalin‐fixed paraffin‐embedded (FFPE) tissues due to RNA degradation associated with this tissue‐embedding method, which is routinely used in oncology workflows.
The 10x Genomics Visium Spatial Gene Expression Solution for FFPE tissue overcomes these limitations, enabling spatial gene expression analysis of FFPE tissues combined with classical histology staining techniques such as Hematoxylin & Eosin (H&E) staining and immunofluorescence.
Methods
We used the 10x Genomics Visium Spatial Gene Expression Solution for FFPE tissue to analyze and resolve tumorigenic profiles in sections of normal and adenocarcinoma prostate samples. This assay incorporates ~5,000 molecularly barcoded, spatially encoded capture probes in spots over which the tissue is placed, imaged, and permeabilized. Imaging and sequencing data are processed together, resulting in a spatially resolved transcriptional readout.
Results
We profiled the whole transcriptome in normal, invasive adenocarcinoma, and acinar cell carcinoma FFPE human prostate tissues.
Unsupervised clustering of the whole transcriptome data from normal, invasive adenocarcinoma, and acinar cell prostate carcinoma FFPE sections enabled the identification of 2 different regions, which had a well defined spatial distribution within the tissues. Well known prostate gland and prostate‐cancer markers were over‐expressed in the corresponding healthy and cancerous portions of the tissue, validating the performance of this method. We found that, while in healthy tissues basal cells and luminal cells are spatially organized, this pattern is lost in tumor samples, where luminal cells are greatly expanded in the invasive carcinoma region and do not colocalize with basal cells. Moreover, T lymphocytes are dispersed throughout the whole tissue section in the adenocarcinoma, while plasma B cells are located in the peritumoral region which could impact prognosis.
Conclusions
Spatial whole transcriptome analysis opens new opportunities for better understanding the TME which can not only help discover novel predictive tumor biomarkers, but also enable identifying cell type and tumor region specific drug targets.
The single cell is considered the basic unit of biology, and the pursuit of understanding how heterogeneous populations of cells can functionally coexist in tissues, organisms, microbial ecosystems, ...and even cancer, makes them the subject of intense study. Next-generation sequencing (NGS) of RNA and DNA has opened a new frontier of (single)-cell biology. Hundreds to millions of cells now can be assayed in parallel, providing the molecular profile of each cell in its milieu inexpensively and in a manner that can be analyzed mathematically. The goal of this article is to provide a high-level overview of single-cell sequencing for the nonexpert and show how its applications are influencing both basic and applied clinical studies in embryology, developmental genetics, and cancer.
Existence of intra tumor heterogeneity has been long established and investigated through multiple traditional methods. The development of single cell sequencing has provided us with great tools to ...probe heterogeneity in cancer genome, transcriptome and epigenome. However, mechanisms of cancer can truly be understood only through multi-omic analysis. Here we developed a new method, Genome Transcriptome One-tube (GTO) that simultaneously sequences the DNA and RNA of a single cell in a single tube. The method was tested in in-vitro cell lines to ensure its robustness before it was applied to study heterogeneity in a transplantation model of pancreatic cancer. Through our analysis of CNA profiles generated from single cells, we were able to identify a clonal metastatic population. Gene expression data from the same cells allowed us to observe the heterogeneity in clonally similar populations. We were also able to observe the transcriptome changes between cells in the same metastatic clone but belonging to different tumor sites (primary and metastasis). This study demonstrates the benefits and need of multi-omic analysis. In a separate study, published in Genome Research, we examined resistance to BRAF inhibitors in melanoma to highlight the diverse applications of single cell sequencing. We performed single cell RNA sequencing on drug resistant populations of melanoma cell lines to evaluate the heterogeneity. Though the resistant cells were not as diverse as we expected, we were able to identify a population of cells transitioning from drug responsive to drug resistant. We identified markers for this population and validated them.
The tumor microenvironment (TME) is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. The constant interaction between a tumor and ...its microenvironment plays a critical role in how the cancer develops, progresses, and responds to therapies. Traditionally, Hematoxylin and Eosin (H&E) and immunohistochemistry staining have been used to annotate and characterize tissues and associated pathologies. Recent single analyte approaches spatially interrogate targeted or transcriptome‐wide expression of RNA in tissue sections, while others capture phenotypes using a limited number of protein markers. However, for a more comprehensive understanding of the unique characteristics of cell types, cell states, and cell‐cell interactions within the TME, analysis of multiple analytes is necessary.
Here we demonstrate a novel, streamlined multiomic spatial assay that integrates histological staining and imaging with simultaneous transcriptome‐wide gene expression and highly multiplexed protein expression profiling from the same formalin‐fixed paraffin embedded (FFPE) tissue section. In short, tissue sections from archived FFPE samples were placed on slides containing arrayed capture oligos with unique positional barcodes. The H&E stained tissues were then imaged, followed by incubation with transcriptome‐wide probes and a high‐plex DNA‐barcoded antibody panel containing intra‐ and extracellular markers. Transcriptome probes and antibody‐barcodes were then spatially captured on the slide and converted into sequencing‐ready libraries. Our data analysis and interactive visualization software enable interrogation of all data layers (H&E morphology, RNA, protein) from the same tissue section.
We apply this method to simultaneously measure gene and protein expression within the TME of human breast cancer and melanoma FFPE samples using whole transcriptome probes and an immune‐oncology antibody panel. The data enables comparison and correlation of multiple analytes and their patterns within the same sample section. In addition, this simultaneous detection enables marker‐guided regional selection and differential gene expression analysis on the defined areas. Taken together, our data demonstrates that a spatially resolved, multiomic approach provides a more comprehensive understanding of cellular behavior in and around tumors, yielding new insights into disease progression, predictive biomarkers, drug response and resistance, and therapeutic development.
Background
Despite years of studies and effort, the best strategies for treating prostate cancer and minimizing the complications of treatment remain unanswered questions. This gap in knowledge is ...partially due to the inability to dissect the complex heterogeneous tumor microenvironment (TME) and immune compartment. Spatially resolved molecular profiling of tumor sections will enhance our understanding of these complexities; However, it has been particularly challenging to do spatial molecular profiling in formalin-fixed paraffin-embedded (FFPE) tissues due to RNA degradation associated with this tissue-embedding method, which is routinely used in oncology workflows. The 10x Genomics Visium Spatial Gene Expression Solution for FFPE tissue overcomes these limitations, enabling spatial gene expression analysis of FFPE tissues combined with classical histology staining techniques such as Hematoxylin & Eosin (H&E) staining and immunofluorescence.
Methods
We used the 10x Genomics Visium Spatial Gene Expression Solution for FFPE tissue to analyze and resolve tumorigenic profiles in sections of normal and adenocarcinoma prostate samples. This assay incorporates ~5,000 molecularly barcoded, spatially encoded capture probes in spots over which the tissue is placed, imaged, and permeabilized. Imaging and sequencing data are processed together, resulting in a spatially resolved transcriptional readout.
Results
We profiled the whole transcriptome in normal, invasive adenocarcinoma, and acinar cell carcinoma FFPE human prostate tissues. Unsupervised clustering of the whole transcriptome data from normal, invasive adenocarcinoma, and acinar cell prostate carcinoma FFPE sections enabled the identification of 2 different regions, which had a well defined spatial distribution within the tissues. Well known prostate gland and prostate-cancer markers were over-expressed in the corresponding healthy and cancerous portions of the tissue, validating the performance of this method. We found that, while in healthy tissues basal cells and luminal cells are spatially organized, this pattern is lost in tumor samples, where luminal cells are greatly expanded in the invasive carcinoma region and do not colocalize with basal cells. Moreover, T lymphocytes are dispersed throughout the whole tissue section in the adenocarcinoma, while plasma B cells are located in the peritumoral region which could impact prognosis.
Conclusions
Spatial whole transcriptome analysis opens new opportunities for better understanding the TME which can not only help discover novel predictive tumor biomarkers, but also enable identifying cell type and tumor region specific drug targets.
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