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 largescale 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.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by ll

BASIC AND TRANSLATIONAL AT cell expansion reduced chronic intestinal inflammation in mice. Strategies to support tuft cells might be developed for treatment of CD.

To comprehensively study extracellular small RNAs (sRNA) by sequencing (sRNA-seq), we developed a novel pipeline to overcome current limitations in analysis entitled, “Tools for Integrative Genome analysis of Extracellular sRNAs (TIGER)”. To demonstrate the power of this tool, sRNA-seq was performed on mouse lipoproteins, bile, urine, and liver samples. A key advance for the TIGER pipeline is the ability to analyze both host and non-host sRNAs at genomic, parent RNA, and individual fragment levels. TIGER was able to identify approximately 60% of sRNAs on lipoproteins, and >85% of sRNAs in liver, bile, and urine, a significant advance compared to existing software. Results suggest that the majority of sRNAs on lipoproteins are non-host sRNAs derived from bacterial sources in the microbiome and environment, specifically rRNA-derived sRNAs from Proteobacteria. Collectively, TIGER facilitated novel discoveries of lipoprotein and biofluid sRNAs and has tremendous applicability for the field of extracellular RNA.

MicroRNAs (miRNA) are shown to be involved in the progression of several types of kidney diseases. Podocytes maintain the integrity of the glomerular basement membrane. Extracellular vesicles (EV) are important in cell-to-cell communication as they can transfer cellular content between cells, including miRNA. However, little is known about how extracellular signals from the glomerular microenvironment regulate podocyte activity. Using a non-contact transwell system, communication between glomerular endothelial cells (GEnC) and podocytes was characterised in-vitro. Identification of transferred EV-miRNAs from GEnC to podocytes was performed using fluorescence cell tracking and miRNA mimetics. To represent kidney disease, podocyte molecular profiling and functions were analysed after EV treatments derived from steady state or activated GEnC. Our data shows activation of GEnC alters EV-miRNA loading, but activation was not found to alter EV secretion. EV delivery of miRNA to recipient podocytes altered cellular miRNA abundance and effector functions in podocytes, including decreased secretion of VEGF and increased mitochondrial stress which lead to altered cellular metabolism and cytoskeletal rearrangement. Finally, results support our hypothesis that miRNA-200c-3p is transfered by EVs from GEnC to podocytes in response to activation, ultimately leading to podocyte dysfunction.

Highlights The results of our study strongly indicate that blocking GRP/GRP-R signaling by targeting GRP-R is sufficient to inhibit ARVs expression. In addition, the combination of blocking GRP/GRP-R signaling (targeting ARVs) and anti-androgens (targeting AR-FL) is a potential new therapeutic approach for treatment of CRPC and therapy-induced tNEPC.

Summary Single-cell RNA sequencing data require several processing procedures to arrive at interpretable results. While commercial platforms can serve as “one-stop shops” for data analysis, they relinquish the flexibility required for customized analyses and are often inflexible between experimental systems. For instance, there is no universal solution for the discrimination of informative or uninformative encapsulated cellular material; thus, pipeline flexibility takes priority. Here, we demonstrate a full data analysis pipeline, constructed modularly from open-source software, including tools that we have contributed. For complete details on the use and execution of this protocol, please refer to Petukhov et al. (2018) , Heiser et al. (2020) , and Heiser and Lau (2020) .

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