Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer.

Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.

Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors.

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

Deciphering the cancer microenvironment from bulk data with EcoTyper.

In this issue of Cell, Luca, Steen et al. develop the EcoTyper software to deconvolve tumor-microenvironment interactions from high volume bulk transcriptomics data. They demonstrate its effectiveness in improving predictions for tumor progression and patient prognosis for a variety of tumor types from multiple data sources.

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities.

We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

Oncogenic K-Ras suppresses global miRNA function.

K-Ras frequently acquires gain-of-function mutations (K-RasG12D being the most common) that trigger significant transcriptomic and proteomic changes to drive tumorigenesis. Nevertheless, oncogenic K-Ras-induced dysregulation of post-transcriptional regulators such as microRNAs (miRNAs) during oncogenesis is poorly understood. Here, we report that K-RasG12D promotes global suppression of miRNA activity, resulting in the upregulation of hundreds of targets. We constructed a comprehensive profile of physiological miRNA targets in mouse colonic epithelium and tumors expressing K-RasG12D using Halo-enhanced Argonaute pull-down. Combining this with parallel datasets of chromatin accessibility, transcriptome, and proteome, we uncovered that K-RasG12D suppressed the expression of Csnk1a1 and Csnk2a1, subsequently decreasing Ago2 phosphorylation at Ser825/829/832/835. Hypo-phosphorylated Ago2 increased binding to mRNAs while reducing its activity to repress miRNA targets. Our findings connect a potent regulatory mechanism of global miRNA activity to K-Ras in a pathophysiological context and provide a mechanistic link between oncogenic K-Ras and the post-transcriptional upregulation of miRNA targets.

Intestinal Tuft Cells: Morphology, Function, and Implications for Human Health.

Tuft cells are a rare and morphologically distinct chemosensory cell type found throughout many organs, including the gastrointestinal tract. These cells were identified by their unique morphologies distinguished by large apical protrusions. Ultrastructural data have begun to describe the molecular underpinnings of their cytoskeletal features, and tuft cell-enriched cytoskeletal proteins have been identified, although the connection of tuft cell morphology to tuft cell functionality has not yet been established. Furthermore, tuft cells display variations in function and identity between and within tissues, leading to the delineation of distinct tuft cell populations. As a chemosensory cell type, they display receptors that are responsive to ligands specific for their environment. While many studies have demonstrated the tuft cell response to protists and helminths in the intestine, recent research has highlighted other roles of tuft cells as well as implicated tuft cells in other disease processes including inflammation, cancer, and viral infections. Here, we review the literature on the cytoskeletal structure of tuft cells. Additionally, we focus on new research discussing tuft cell lineage, ligand-receptor interactions, tuft cell tropism, and the role of tuft cells in intestinal disease. Finally, we discuss the implication of tuft cell-targeted therapies in human health and how the morphology of tuft cells may contribute to their functionality.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.

MOTIVATION: Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. RESULTS: To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

Automated quality control and cell identification of droplet-based single-cell data using dropkick.

A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in data sets with disparate library sizes confounded by high technical noise (i.e., batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining data set-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventional thresholding approaches and EmptyDrops, a popular computational method, showing greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low- and high-background data sets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to data set-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell Python packages.

Dysplastic Stem Cell Plasticity Functions as a Driving Force for Neoplastic Transformation of Precancerous Gastric Mucosa.

BACKGROUND & AIMS: Dysplasia carries a high risk of cancer development; however, the cellular mechanisms for dysplasia evolution to cancer are obscure. We have previously identified 2 putative dysplastic stem cell (DSC) populations, CD44v6neg/CD133+/CD166+ (double positive [DP]) and CD44v6+/CD133+/CD166+ (triple positive [TP]), which may contribute to cellular heterogeneity of gastric dysplasia. Here, we investigated functional roles and cell plasticity of noncancerous Trop2+/CD133+/CD166+ DSCs initially developed in the transition from precancerous metaplasia to dysplasia in the stomach. METHODS: Dysplastic organoids established from active Kras-induced mouse stomachs were used for transcriptome analysis, in vitro differentiation, and in vivo tumorigenicity assessments of DSCs. Cell heterogeneity and genetic alterations during clonal evolution of DSCs were examined by next-generation sequencing. Tissue microarrays were used to identify DSCs in human dysplasia. We additionally evaluated the effect of casein kinase 1 alpha (CK1α) regulation on the DSC activities using both mouse and human dysplastic organoids. RESULTS: We identified a high similarity of molecular profiles between DP- and TP-DSCs, but more dynamic activities of DP-DSCs in differentiation and survival for maintaining dysplastic cell lineages through Wnt ligand-independent CK1α/ß-catenin signaling. Xenograft studies demonstrated that the DP-DSCs clonally evolve toward multiple types of gastric adenocarcinomas and promote cancer cell heterogeneity by acquiring additional genetic mutations and recruiting the tumor microenvironment. Last, growth and survival of both mouse and human dysplastic organoids were controlled by targeting CK1α. CONCLUSIONS: These findings indicate that the DSCs are de novo gastric cancer-initiating cells responsible for neoplastic transformation and a promising target for intervention in early induction of gastric cancer.

Single-Cell Transcriptomics Reveals a Conserved Metaplasia Program in Pancreatic Injury.

BACKGROUND & AIMS: Acinar to ductal metaplasia (ADM) occurs in the pancreas in response to tissue injury and is a potential precursor for adenocarcinoma. The goal of these studies was to define the populations arising from ADM, the associated transcriptional changes, and markers of disease progression. METHODS: Acinar cells were lineage-traced with enhanced yellow fluorescent protein (EYFP) to follow their fate post-injury. Transcripts of more than 13,000 EYFP+ cells were determined using single-cell RNA sequencing (scRNA-seq). Developmental trajectories were generated. Data were compared with gastric metaplasia, KrasG12D-induced neoplasia, and human pancreatitis. Results were confirmed by immunostaining and electron microscopy. KrasG12D was expressed in injury-induced ADM using several inducible Cre drivers. Surgical specimens of chronic pancreatitis from 15 patients were evaluated by immunostaining. RESULTS: scRNA-seq of ADM revealed emergence of a mucin/ductal population resembling gastric pyloric metaplasia. Lineage trajectories suggest that some pyloric metaplasia cells can generate tuft and enteroendocrine cells (EECs). Comparison with KrasG12D-induced ADM identifies populations associated with disease progression. Activation of KrasG12D expression in HNF1B+ or POU2F3+ ADM populations leads to neoplastic transformation and formation of MUC5AC+ gastric-pit-like cells. Human pancreatitis samples also harbor pyloric metaplasia with a similar transcriptional phenotype. CONCLUSIONS: Under conditions of chronic injury, acinar cells undergo a pyloric-type metaplasia to mucinous progenitor-like populations, which seed disparate tuft cell and EEC lineages. ADM-derived EEC subtypes are diverse. KrasG12D expression is sufficient to drive neoplasia when targeted to injury-induced ADM populations and offers an alternative origin for tumorigenesis. This program is conserved in human pancreatitis, providing insight into early events in pancreas diseases.

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images.

MOTIVATION: Multiplexed imaging is a nascent single-cell assay with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available. RESULTS: We implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Furthermore, we find that dividing multiplexed imaging data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in multiplexed imaging. AVAILABILITY AND IMPLEMENTATION: Source code is provided at: https://github.com/statimagcoll/MultiplexedNormalization and an R package to implement these methods is available here: https://github.com/ColemanRHarris/mxnorm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combinatorial Transcriptional Profiling of Mouse and Human Enteric Neurons Identifies Shared and Disparate Subtypes In Situ.

BACKGROUND & AIMS: The enteric nervous system (ENS) coordinates essential intestinal functions through the concerted action of diverse enteric neurons (ENs). However, integrated molecular knowledge of EN subtypes is lacking. To compare human and mouse ENs, we transcriptionally profiled healthy ENS from adult humans and mice. We aimed to identify transcripts marking discrete neuron subtypes and visualize conserved EN subtypes for humans and mice in multiple bowel regions. METHODS: Human myenteric ganglia and adjacent smooth muscle were isolated by laser-capture microdissection for RNA sequencing. Ganglia-specific transcriptional profiles were identified by computationally subtracting muscle gene signatures. Nuclei from mouse myenteric neurons were isolated and subjected to single-nucleus RNA sequencing, totaling more than 4 billion reads and 25,208 neurons. Neuronal subtypes were defined using mouse single-nucleus RNA sequencing data. Comparative informatics between human and mouse data sets identified shared EN subtype markers, which were visualized in situ using hybridization chain reaction. RESULTS: Several EN subtypes in the duodenum, ileum, and colon are conserved between humans and mice based on orthologous gene expression. However, some EN subtype-specific genes from mice are expressed in completely distinct morphologically defined subtypes in humans. In mice, we identified several neuronal subtypes that stably express gene modules across all intestinal segments, with graded, regional expression of 1 or more marker genes. CONCLUSIONS: Our combined transcriptional profiling of human myenteric ganglia and mouse EN provides a rich foundation for developing novel intestinal therapeutics. There is congruency among some EN subtypes, but we note multiple species differences that should be carefully considered when relating findings from mouse ENS research to human gastrointestinal studies.

MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images.

Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is the lack of robust cell segmentation tools for tissue sections. We present Multiplexed Image Resegmentation of Internal Aberrant Membranes (MIRIAM) that combines (a) a pipeline for cell segmentation and quantification that incorporates machine learning-based pixel classification to define cellular compartments, (b) a novel method for extending incomplete cell membranes, and (c) a deep learning-based cell shape descriptor. Using human colonic adenomas as an example, we show that MIRIAM is superior to widely utilized segmentation methods and provides a pipeline that is broadly applicable to different imaging platforms and tissue types.

DSS-induced damage to basement membranes is repaired by matrix replacement and crosslinking.

Basement membranes are an ancient form of animal extracellular matrix. As important structural and functional components of tissues, basement membranes are subject to environmental damage and must be repaired while maintaining functions. Little is known about how basement membranes get repaired. This paucity stems from a lack of suitable in vivo models for analyzing such repair. Here, we show that dextran sodium sulfate (DSS) directly damages the gut basement membrane when fed to adult Drosophila DSS becomes incorporated into the basement membrane, promoting its expansion while decreasing its stiffness, which causes morphological changes to the underlying muscles. Remarkably, two days after withdrawal of DSS, the basement membrane is repaired by all measures of analysis. We used this new damage model to determine that repair requires collagen crosslinking and replacement of damaged components. Genetic and biochemical evidence indicates that crosslinking is required to stabilize the newly incorporated repaired Collagen IV rather than to stabilize the damaged Collagen IV. These results suggest that basement membranes are surprisingly dynamic.

Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features.

Single cells are the building blocks of tissue systems that determine organ phenotypes, behaviors, and functions. Understanding the differences between cell types and their activities might provide us with insights into normal tissue physiology, development of disease, and new therapeutic strategies. Although -omic level single-cell technologies are a relatively recent development that have been used only in research settings, these approaches might eventually be used in the clinic. We review the prospects of applying single-cell genome, transcriptome, epigenome, proteome, and metabolome analyses to gastroenterology and hepatology research. Combining data from multi-omic platforms coupled to rapid technological development could lead to new diagnostic, prognostic, and therapeutic approaches.

Succinate Produced by Intestinal Microbes Promotes Specification of Tuft Cells to Suppress Ileal Inflammation.

BACKGROUND & AIMS: Countries endemic for parasitic infestations have a lower incidence of Crohn's disease (CD) than nonendemic countries, and there have been anecdotal reports of the beneficial effects of helminths in CD patients. Tuft cells in the small intestine sense and direct the immune response against eukaryotic parasites. We investigated the activities of tuft cells in patients with CD and mouse models of intestinal inflammation. METHODS: We used microscopy to quantify tuft cells in intestinal specimens from patients with ileal CD (n = 19), healthy individuals (n = 14), and TNFΔARE/+ mice, which develop Crohn's-like ileitis. We performed single-cell RNA sequencing, mass spectrometry, and microbiome profiling of intestinal tissues from wild-type and Atoh1-knockout mice, which have expansion of tuft cells, to study interactions between microbes and tuft cell populations. We assessed microbe dependence of tuft cell populations using microbiome depletion, organoids, and microbe transplant experiments. We used multiplex imaging and cytokine assays to assess alterations in inflammatory response following expansion of tuft cells with succinate administration in TNFΔARE/+ and anti-CD3E CD mouse models. RESULTS: Inflamed ileal tissues from patients and mice had reduced numbers of tuft cells, compared with healthy individuals or wild-type mice. Expansion of tuft cells was associated with increased expression of genes that regulate the tricarboxylic acid cycle, which resulted from microbe production of the metabolite succinate. Experiments in which we manipulated the intestinal microbiota of mice revealed the existence of an ATOH1-independent population of tuft cells that was sensitive to metabolites produced by microbes. Administration of succinate to mice expanded tuft cells and reduced intestinal inflammation in TNFΔARE/+ mice and anti-CD3E-treated mice, increased GATA3+ cells and type 2 cytokines (IL22, IL25, IL13), and decreased RORGT+ cells and type 17 cytokines (IL23) in a tuft cell-dependent manner. CONCLUSIONS: We found that tuft cell expansion reduced chronic intestinal inflammation in mice. Strategies to expand tuft cells might be developed for treatment of CD.

Quantitative assessment of cell population diversity in single-cell landscapes.

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.

Dual indexed library design enables compatibility of in-Drop single-cell RNA-sequencing with exAMP chemistry sequencing platforms.

BACKGROUND: The increasing demand of single-cell RNA-sequencing (scRNA-seq) experiments, such as the number of experiments and cells queried per experiment, necessitates higher sequencing depth coupled to high data quality. New high-throughput sequencers, such as the Illumina NovaSeq 6000, enables this demand to be filled in a cost-effective manner. However, current scRNA-seq library designs present compatibility challenges with newer sequencing technologies, such as index-hopping, and their ability to generate high quality data has yet to be systematically evaluated. RESULTS: Here, we engineered a dual-indexed library structure, called TruDrop, on top of the inDrop scRNA-seq platform to solve these compatibility challenges, such that TruDrop libraries and standard Illumina libraries can be sequenced alongside each other on the NovaSeq. On scRNA-seq libraries, we implemented a previously-documented countermeasure to the well-described problem of index-hopping, demonstrated significant improvements in base-calling accuracy on the NovaSeq, and provided an example of multiplexing twenty-four scRNA-seq libraries simultaneously. We showed favorable comparisons in transcriptional diversity of TruDrop compared with prior inDrop libraries. CONCLUSIONS: Our approach enables cost-effective, high throughput generation of sequencing data with high quality, which should enable more routine use of scRNA-seq technologies.

pyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction.

MOTIVATION: The emergence of single-cell RNA-sequencing has enabled analyses that leverage transitioning cell states to reconstruct pseudotemporal trajectories. Multidimensional data sparsity, zero inflation and technical variation necessitate the selection of high-quality features that feed downstream analyses. Despite the development of numerous algorithms for the unsupervised selection of biologically relevant features, their differential performance remains largely unaddressed. RESULTS: We implemented the neighborhood variance ratio (NVR) feature selection approach as a Python package with substantial improvements in performance. In comparing NVR with multiple unsupervised algorithms such as dpFeature, we observed striking differences in features selected. We present evidence that quantifiable dataset properties have observable and predictable effects on the performance of these algorithms. AVAILABILITY AND IMPLEMENTATION: pyNVR is freely available at https://github.com/KenLauLab/NVR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.