A Proximal-to-Distal Survey of Healthy Adult Human Small Intestine and Colon Epithelium by Single-Cell Transcriptomics

BACKGROUND & AIMS

Single-cell transcriptomics offer unprecedented resolution of tissue function at the cellular level, yet studies analyzing healthy adult human small intestine and colon are sparse. Here, we present single-cell transcriptomics covering the duodenum, jejunum, ileum, and ascending, transverse, and descending colon from 3 human beings.

METHODS

A total of 12,590 single epithelial cells from 3 independently processed organ donors were evaluated for organ-specific lineage biomarkers, differentially regulated genes, receptors, and drug targets. Analyses focused on intrinsic cell properties and their capacity for response to extrinsic signals along the gut axis across different human beings.

RESULTS

Cells were assigned to 25 epithelial lineage clusters. Multiple accepted intestinal stem cell markers do not specifically mark all human intestinal stem cells. Lysozyme expression is not unique to human Paneth cells, and Paneth cells lack expression of expected niche factors. Bestrophin 4 (BEST4)⁺ cells express Neuropeptide Y (NPY) and show maturational differences between the small intestine and colon. Tuft cells possess a broad ability to interact with the innate and adaptive immune systems through previously unreported receptors. Some classes of mucins, hormones, cell junctions, and nutrient absorption genes show unappreciated regional expression differences across lineages. The differential expression of receptors and drug targets across lineages show biological variation and the potential for variegated responses.

CONCLUSIONS

Our study identifies novel lineage marker genes, covers regional differences, shows important differences between mouse and human gut epithelium, and reveals insight into how the epithelium responds to the environment and drugs. This comprehensive cell atlas of the healthy adult human intestinal epithelium resolves likely functional differences across anatomic regions along the gastrointestinal tract and advances our understanding of human intestinal physiology.

The structure of the human cell cycle

Understanding the organization of the cell cycle has been a longstanding goal in cell biology. We combined time-lapse microscopy, highly multiplexed single-cell imaging of 48 core cell cycle proteins, and manifold learning to render a visualization of the human cell cycle. This data-driven approach revealed the comprehensive "structure" of the cell cycle: a continuum of molecular states that cells occupy as they transition from one cell division to the next, or as they enter or exit cell cycle arrest. Paradoxically, progression deeper into cell cycle arrest was accompanied by increases in proliferative effectors such as CDKs and cyclins, which can drive cell cycle re-entry by overcoming p21 induction. The structure also revealed the molecular trajectories into senescence and the unique combination of molecular features that define this irreversibly arrested state. This approach will enable the comparison of alternative cell cycles during development, in response to environmental perturbation and in disease. A record of this paper's transparent peer review process is included in the supplemental information.

Interleukin-8 mediates neutrophil-endothelial interactions in pig-to-human xenogeneic models

BACKGROUND

Human neutrophils are sequestered by pig lung xenografts within minutes during ex vivo perfusion. This phenomenon is not prevented by pig genetic modifications that remove xeno-antigens or added human regulatory molecules intended to down-regulate activation of complement and coagulation pathways. This study investigated whether recipient and donor interleukin-8 (IL-8), a chemokine known to attract and activate neutrophils during inflammation, is elaborated in the context of xenogeneic injury, and whether human or pig IL-8 promote the adhesion of human neutrophils in in vitro xenograft models.

METHODS

Plasma levels of pig, human or non-human primate (NHP) IL-8 from ex vivo pig lung perfusion experiments (n = 10) and in vivo pig-to-baboon lung transplantation in baboons (n = 22) were analysed by ELISA or Luminex. Human neutrophils stimulated with human or pig IL-8 were analysed for CD11b expression, CD18 activation, oxidative burst and adhesion to resting or TNF-activated endothelial cells (EC) evaluated under static and flow (Bioflux) conditions. For some experiments, human neutrophils were incubated with Reparixin (IL-8/CXCL8 receptor blocker) and then analysed as in the in vitro experiments mentioned above.

RESULTS

Plasma levels of pig IL-8 (~6113 pg/mL) increased more than human (~1235 pg/mL) between one and four hours after initiation of ex vivo lung perfusion. However, pig IL-8 levels remained consistently low (<60 pg/mL) and NHP IL-8 plasma levels increased by ~2000 pg/mL after four hours in a pig-to-baboon lung xenotransplantation. In vitro, human neutrophils' CD11b expression, CD18 activation and oxidative burst all increased in a dose-dependent manner following exposure to either pig or human IL-8, which also were associated with increased adhesion to EC in both static and flow conditions. Reparixin inhibited human neutrophil activation by both pig and human IL-8 in a dose-dependent fashion. At 0.1 mg/mL, Reparixin inhibited the adhesion of IL-8-activated human neutrophils to pAECs by 84 ± 2.5%.

CONCLUSIONS

Pig IL-8 increased in an ex vivo model of pig-to-human lung xenotransplantation but is not detected in vivo, whereas human or NHP IL-8 is elevated to a similar degree in both models. Both pig and human IL-8 activate human neutrophils and increase their adhesion to pig aortic ECs, a process significantly inhibited by the addition of Reparixin to human neutrophils. This work implicates IL-8, whether of pig or human origin, as a possible factor mediating in lung xenograft inflammation and injury and supports the evaluation of therapeutic targeting of this pathway in the context of xenotransplantation.

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

BACKGROUND

Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics.

RESULTS

Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods.

CONCLUSIONS

This work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

Dominant CD4+ T cell receptors remain stable throughout antiretroviral therapy-mediated immune restoration in people with HIV

In people with HIV (PWH), the post-antiretroviral therapy (ART) window is critical for immune restoration and HIV reservoir stabilization. We employ deep immune profiling and T cell receptor (TCR) sequencing and examine proliferation to assess how ART impacts T cell homeostasis. In PWH on long-term ART, lymphocyte frequencies and phenotypes are mostly stable. By contrast, broad phenotypic changes in natural killer (NK) cells, γδ T cells, B cells, and CD4+ and CD8+ T cells are observed in the post-ART window. Whereas CD8+ T cells mostly restore, memory CD4+ T subsets and cytolytic NK cells show incomplete restoration 1.4 years post ART. Surprisingly, the hierarchies and frequencies of dominant CD4 TCR clonotypes (0.1%-11% of all CD4+ T cells) remain stable post ART, suggesting that clonal homeostasis can be independent of homeostatic processes regulating CD4+ T cell absolute number, phenotypes, and function. The slow restoration of host immunity post ART also has implications for the design of ART interruption studies.

Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells

The CDK4/6 inhibitor palbociclib blocks cell cycle progression in Estrogen receptor-positive, human epidermal growth factor 2 receptor-negative (ER+/HER2-) breast tumor cells. Despite the drug's success in improving patient outcomes, a small percentage of tumor cells continues to divide in the presence of palbociclib-a phenomenon we refer to as fractional resistance. It is critical to understand the cellular mechanisms underlying fractional resistance because the precise percentage of resistant cells in patient tissue is a strong predictor of clinical outcomes. Here, we hypothesize that fractional resistance arises from cell-to-cell differences in core cell cycle regulators that allow a subset of cells to escape CDK4/6 inhibitor therapy. We used multiplex, single-cell imaging to identify fractionally resistant cells in both cultured and primary breast tumor samples resected from patients. Resistant cells showed premature accumulation of multiple G1 regulators including E2F1, retinoblastoma protein, and CDK2, as well as enhanced sensitivity to pharmacological inhibition of CDK2 activity. Using trajectory inference approaches, we show how plasticity among cell cycle regulators gives rise to alternate cell cycle "paths" that allow individual tumor cells to escape palbociclib treatment. Understanding drivers of cell cycle plasticity, and how to eliminate resistant cell cycle paths, could lead to improved cancer therapies targeting fractionally resistant cells to improve patient outcomes.

DELVE: feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .

Feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or noisy and irrelevant features (e.g. measurements with low signal-to-noise ratio) can mask the underlying trajectory of study and hinder inference. Here, we present DELVE (dynamic selection of locally covarying features), an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that recapitulates cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference, and instead models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of the cell cycle and cellular differentiation, we demonstrate that DELVE selects features that more accurately characterize cell populations and improve the recovery of cell type transitions. This feature selection framework provides an alternative approach for improving trajectory inference and uncovering co-variation amongst features along a biological trajectory. DELVE is implemented as an open-source python package and is publicly available at: https://github.com/jranek/delve.

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control

Despite being heavily infiltrated by immune cells, tuberculosis (TB) granulomas often subvert the host response to Mycobacterium tuberculosis (Mtb) infection and support bacterial persistence. We previously discovered that human TB granulomas are enriched for immunosuppressive factors typically associated with tumor-immune evasion, raising the intriguing possibility that they promote tolerance to infection. In this study, our goal was to identify the prime drivers for establishing this tolerogenic niche and to determine if the magnitude of this response correlates with bacterial persistence. To do this, we conducted a multimodal spatial analysis of 52 granulomas from 16 non-human primates (NHP) who were infected with low dose Mtb for 9-12 weeks. Notably, each granuloma's bacterial burden was individually quantified allowing us to directly ask how granuloma spatial structure and function relate to infection control. We found that a universal feature of TB granulomas was partitioning of the myeloid core into two distinct metabolic environments, one of which is hypoxic. This hypoxic environment associated with pathologic immune cell states, dysfunctional cellular organization of the granuloma, and a near-complete blockade of lymphocyte infiltration that would be required for a successful host response. The extent of these hypoxia-associated features correlated with worsened bacterial burden. We conclude that hypoxia governs immune cell state and organization within granulomas and is a potent driver of subverted immunity during TB.

Knock-out of N-glycolylneuraminic acid attenuates antibody-mediated rejection in xenogenically perfused porcine lungs

BACKGROUND

Antibody-mediated rejection has long been known to be one of the major organ failure mechanisms in xenotransplantation. In addition to the porcine α1,3-galactose (α1,3Gal) epitope, N-Glycolylneuraminic acid (Neu5Gc), a sialic acid, has been identified as an important porcine antigen against which most humans have pre-formed antibodies. Here we evaluate GalTKO.hCD46 lungs with an additional cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) gene knock-out (Neu5GcKO) in a xenogeneic ex vivo perfusion model METHODS: Eleven GalTKO.hCD46.Neu5GcKO pig lungs were perfused for up to 6 h with fresh heparinized human blood. Six of them were treated with histamine (H) blocker famotidine and 1-thromboxane synthase inhibitor Benzylimidazole (BIA) and five were left untreated. GalTKO.hCD46 lungs without Neu5GcKO (n = 18: eight untreated and 10 BIA+H treated) served as a reference. Functional parameters, blood, and tissue samples were collected at pre-defined time points throughout the perfusion RESULTS: All but one Neu5GcKO organs maintained adequate blood oxygenation and "survived" until elective termination at 6 h whereas two reference lungs failed before elective termination at 4 h. Human anti-Neu5Gc antibody serum levels decreased during the perfusion of GalTKO.hCD46 lungs by flow cytometry (∼40% IgM, 60% IgG), whereas antibody levels in Neu5GcKO lung perfusions did not fall (IgM p = .007; IgG p < .001). Thromboxane elaboration, thrombin generation, and histamine levels were significantly reduced with Neu5GcKO lungs compared to reference in the untreated groups (p = .007, .005, and .037, respectively); treatment with BIA+H masked these changes. Activation of platelets, measured as CD62P expression on circulating platelets, was lower in Neu5GcKO experiments compared to reference lungs (p = .023), whereas complement activation (as C3a rise in plasma) was not altered. MCP-1 and lactotransferin level elevations were blunted in Neu5GcKO lung perfusions (p = .007 and .032, respectively). Pulmonary vascular resistance (PVR) rise was significantly attenuated and delayed in untreated GalTKO.hCD46.Neu5GcKO lungs in comparison to the untreated GalTKO.hCD46 lungs (p = .003) CONCLUSION: Additional Neu5GcKO in GalTKO.hCD46 lungs significantly reduces parameters associated with antibody-mediated inflammation and activation of the coagulation cascade. Knock-out of the Neu5Gc sialic acid should be beneficial to reduce innate immune antigenicity of porcine lungs in future human recipients.

Automated classification of cellular expression in multiplexed imaging data with Nimbus

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) evolution, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T-cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.