Single-cell analysis of bidirectional reprogramming between early embryonic states identify mechanisms of differential lineage plasticities in mice.

Two distinct lineages, pluripotent epiblast (EPI) and primitive (extra-embryonic) endoderm (PrE), arise from common inner cell mass (ICM) progenitors in mammalian embryos. To study how these sister identities are forged, we leveraged mouse embryonic stem (ES) cells and extra-embryonic endoderm (XEN) stem cells-in vitro counterparts of the EPI and PrE. Bidirectional reprogramming between ES and XEN coupled with single-cell RNA and ATAC-seq analyses showed distinct rates, efficiencies, and trajectories of state conversions, identifying drivers and roadblocks of reciprocal conversions.

The commitment of the human cell atlas to humanity.

The Human Cell Atlas (HCA) is a global partnership "to create comprehensive reference maps of all human cells-the fundamental units of life - as a basis for both understanding human health and diagnosing, monitoring, and treating disease." ( https://www.humancellatlas.

Progressive plasticity during colorectal cancer metastasis.

As cancers progress, they become increasingly aggressive-metastatic tumours are less responsive to first-line therapies than primary tumours, they acquire resistance to successive therapies and eventually cause death. Mutations are largely conserved between primary and metastatic tumours from the same patients, suggesting that non-genetic phenotypic plasticity has a major role in cancer progression and therapy resistance. However, we lack an understanding of metastatic cell states and the mechanisms by which they transition.

Precursor central memory versus effector cell fate and naïve CD4+ T cell heterogeneity.

Upon antigenic stimulation, naïve CD4+ T cells can give rise to phenotypically distinct effector T helper cells and long-lived memory T cells. We computationally reconstructed the in vivo trajectory of CD4+ T cell differentiation during a type I inflammatory immune response and identified two distinct differentiation paths for effector and precursor central memory T cells arising directly from naïve CD4+ T cells. Unexpectedly, our studies revealed heterogeneity among naïve CD4+ T cells, which are typically considered homogeneous save for their diverse T cell receptor usage.

Distinct tumor architectures and microenvironments for the initiation of breast cancer metastasis in the brain.

Brain metastasis, a serious complication of cancer, hinges on the initial survival, microenvironment adaptation, and outgrowth of disseminated cancer cells. To understand the early stages of brain colonization, we investigated two prevalent sources of cerebral relapse, triple-negative (TNBC) and HER2+ (HER2BC) breast cancers. Using mouse models and human tissue samples, we found that these tumor types colonize the brain, with a preference for distinctive tumor architectures, stromal interfaces, and autocrine programs.

Age-related epithelial defects limit thymic function and regeneration.

The thymus is essential for establishing adaptive immunity yet undergoes age-related involution that leads to compromised immune responsiveness. The thymus is also extremely sensitive to acute insult and although capable of regeneration, this capacity declines with age for unknown reasons. We applied single-cell and spatial transcriptomics, lineage-tracing and advanced imaging to define age-related changes in nonhematopoietic stromal cells and discovered the emergence of two atypical thymic epithelial cell (TEC) states.

REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data.

Motivation: Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis-region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites.

Results: We develop REUNION, a framework for predicting genome-wide TF binding and cis-region-TF-gene "triplet" regulatory associations using single-cell multi-omics data.

CellRank 2: unified fate mapping in multiview single-cell data.

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development.

Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers.

Optimal transport (OT) and the related Wasserstein metric are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS).

The neuroendocrine transition in prostate cancer is dynamic and dependent on ASCL1.

Lineage plasticity is a recognized hallmark of cancer progression that can shape therapy outcomes. The underlying cellular and molecular mechanisms mediating lineage plasticity remain poorly understood. Here, we describe a versatile platform to identify and interrogate the molecular determinants of neuroendocrine lineage transformation at different stages of prostate cancer progression.

The covariance environment defines cellular niches for spatial inference.

A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells.

Metastatic site influences driver gene function in pancreatic cancer.

Driver gene mutations can increase the metastatic potential of the primary tumor, but their role in sustaining tumor growth at metastatic sites is poorly understood. A paradigm of such mutations is inactivation of - a transcriptional effector of TGFβ signaling - which is a hallmark of multiple gastrointestinal malignancies. inactivation mediates TGFβ's remarkable anti- to pro-tumorigenic switch during cancer progression and can thus influence both tumor initiation and metastasis.

Microbial cancer immunotherapy reprograms hematopoietic stem cells to enhance anti-tumor immunity.

Mycobacterium bovis BCG is the vaccine against tuberculosis and an immunotherapy for bladder cancer. When administered intravenously, BCG reprograms bone marrow hematopoietic stem and progenitor cells (HSPCs), leading to heterologous protection against infections. Whether HSPC-reprogramming contributes to the anti-tumor effects of BCG administered into the bladder is unknown.

Novel Approach for Pancreas Transcriptomics Reveals the Cellular Landscape in Homeostasis and Acute Pancreatitis.

Background & Aims: Acinar cells produce digestive enzymes that impede transcriptomic characterization of the exocrine pancreas. Thus, single-cell RNA-sequencing studies of the pancreas underrepresent acinar cells relative to histological expectations, and a robust approach to capture pancreatic cell responses in disease states is needed. We sought to innovate a method that overcomes these challenges to accelerate study of the pancreas in health and disease.

Joint representation and visualization of derailed cell states with Decipher.

Biological insights often depend on comparing conditions such as disease and health, yet we lack effective computational tools for integrating single-cell genomics data across conditions or characterizing transitions from normal to deviant cell states. Here, we present Decipher, a deep generative model that characterizes derailed cell-state trajectories. Decipher jointly models and visualizes gene expression and cell state from normal and perturbed single-cell RNA-seq data, revealing shared and disrupted dynamics.

Supervised discovery of interpretable gene programs from single-cell data.

Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation.

Progressive plasticity during colorectal cancer metastasis.

Metastasis is the principal cause of cancer death, yet we lack an understanding of metastatic cell states, their relationship to primary tumor states, and the mechanisms by which they transition. In a cohort of biospecimen trios from same-patient normal colon, primary and metastatic colorectal cancer, we show that while primary tumors largely adopt LGR5 intestinal stem-like states, metastases display progressive plasticity. Loss of intestinal cell states is accompanied by reprogramming into a highly conserved fetal progenitor state, followed by non-canonical differentiation into divergent squamous and neuroendocrine-like states, which is exacerbated by chemotherapy and associated with poor patient survival.

scKINETICS: inference of regulatory velocity with single-cell transcriptomics data.

Motivation: Transcriptional dynamics are governed by the action of regulatory proteins and are fundamental to systems ranging from normal development to disease. RNA velocity methods for tracking phenotypic dynamics ignore information on the regulatory drivers of gene expression variability through time.

Results: We introduce scKINETICS (Key regulatory Interaction NETwork for Inferring Cell Speed), a dynamical model of gene expression change which is fit with the simultaneous learning of per-cell transcriptional velocities and a governing gene regulatory network.

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population.