Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets

Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.

Faecal microbial transfer and complex carbohydrates mediate protection against COPD

OBJECTIVE

Chronic obstructive pulmonary disease (COPD) is a major cause of global illness and death, most commonly caused by cigarette smoke. The mechanisms of pathogenesis remain poorly understood, limiting the development of effective therapies. The gastrointestinal microbiome has been implicated in chronic lung diseases via the gut-lung axis, but its role is unclear.

DESIGN

Using an in vivo mouse model of cigarette smoke (CS)-induced COPD and faecal microbial transfer (FMT), we characterised the faecal microbiota using metagenomics, proteomics and metabolomics. Findings were correlated with airway and systemic inflammation, lung and gut histopathology and lung function. Complex carbohydrates were assessed in mice using a high resistant starch diet, and in 16 patients with COPD using a randomised, double-blind, placebo-controlled pilot study of inulin supplementation.

RESULTS

FMT alleviated hallmark features of COPD (inflammation, alveolar destruction, impaired lung function), gastrointestinal pathology and systemic immune changes. Protective effects were additive to smoking cessation, and transfer of CS-associated microbiota after antibiotic-induced microbiome depletion was sufficient to increase lung inflammation while suppressing colonic immunity in the absence of CS exposure. Disease features correlated with the relative abundance of Muribaculaceae, Desulfovibrionaceae and Lachnospiraceae family members. Proteomics and metabolomics identified downregulation of glucose and starch metabolism in CS-associated microbiota, and supplementation of mice or human patients with complex carbohydrates improved disease outcomes.

CONCLUSION

The gut microbiome contributes to COPD pathogenesis and can be targeted therapeutically.

Immune Modulation with RANKL Blockade through Denosumab Treatment in Patients with Cancer

Denosumab is a fully human mAb that binds receptor activator of NFκB ligand (RANKL). It is routinely administered to patients with cancer to reduce the incidence of new bone metastasis. RANK-RANKL interactions regulate bone turnover by controlling osteoclast recruitment, development, and activity. However, these interactions also can regulate immune cells including dendritic cells and medullary thymic epithelial cells. Inhibition of the latter results in reduced thymic negative selection of T cells and could enhance the generation of tumor-specific T cells. We examined whether administering denosumab could modify modulate circulating immune cells in patients with cancer. Blood was collected from 23 patients with prostate cancer and 3 patients with renal cell carcinoma, all of whom had advanced disease and were receiving denosumab, prior to and during denosumab treatment. Using high-dimensional mass cytometry, we found that denosumab treatment by itself induced modest effects on circulating immune cell frequency and activation. We also found minimal changes in the circulating T-cell repertoire and the frequency of new thymic emigrants with denosumab treatment. However, when we stratified patients by whether they were receiving chemotherapy and/or steroids, patients receiving these concomitant treatments showed significantly greater immune modulation, including an increase in the frequency of natural killer cells early and classical monocytes later. We also saw broad induction of CTLA-4 and TIM3 expression in circulating lymphocytes and some monocyte populations. These findings suggest that denosumab treatment by itself has modest immunomodulatory effects, but when combined with conventional cancer treatments, can lead to the induction of immunologic checkpoints. See related Spotlight by Nasrollahi and Davar, p. 383.

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. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.

Meloxicam treatment disrupts the regional structure of innate inflammation sites by targeting the pro-inflammatory effects of prostanoids

BACKGROUND AND PURPOSE

Non-steroidal anti-inflammatory drugs (NSAIDs) are the most widely prescribed drugs in the world due to their analgesic, antipyretic and anti-inflammatory effects. However, NSAIDs inhibit prostanoid synthesis, interfering with their pro-inflammatory and anti-inflammatory functions and potentially prolonging acute inflammation.

EXPERIMENTAL APPROACH

We used high-content immunohistochemistry to define the impact of meloxicam treatment on spatially separated pro-inflammatory and anti-inflammatory processes during innate inflammation in mice induced by zymosan. This allowed us to determine the effect of meloxicam treatment on the organization of pro-inflammatory and anti-inflammatory microenvironments, thereby identifying relevant changes in immune cell localization, recruitment and activation.

KEY RESULTS

Meloxicam treatment reduced zymosan-induced thermal hypersensitivity at early time points but delayed its resolution. High-content immunohistochemistry revealed that the pro-inflammatory area was smaller after treatment, diminishing neutrophil recruitment, M1-like macrophage polarization, and especially phagocytosis by neutrophils and macrophages. The polarization of macrophages towards the M2-like anti-inflammatory phenotype was unaffected, and the number of anti-inflammatory eosinophils actually increased.

CONCLUSION AND IMPLICATIONS

High-content immunohistochemistry was able to identify relevant meloxicam-mediated effects on inflammatory processes based on alterations in the regional structure of inflammation sites. Meloxicam delayed the clearance of pathogens by inhibiting pro-inflammatory processes, causing prolonged inflammation. Our data suggest that the prescription of NSAIDs as a treatment during an acute pathogen-driven inflammation should be reconsidered in patients with compromised immune systems.

Depleting myeloid-biased haematopoietic stem cells rejuvenates aged immunity

Ageing of the immune system is characterized by decreased lymphopoiesis and adaptive immunity, and increased inflammation and myeloid pathologies1,2. Age-related changes in populations of self-renewing haematopoietic stem cells (HSCs) are thought to underlie these phenomena3. During youth, HSCs with balanced output of lymphoid and myeloid cells (bal-HSCs) predominate over HSCs with myeloid-biased output (my-HSCs), thereby promoting the lymphopoiesis required for initiating adaptive immune responses, while limiting the production of myeloid cells, which can be pro-inflammatory4. Ageing is associated with increased proportions of my-HSCs, resulting in decreased lymphopoiesis and increased myelopoiesis3,5,6. Transfer of bal-HSCs results in abundant lymphoid and myeloid cells, a stable phenotype that is retained after secondary transfer; my-HSCs also retain their patterns of production after secondary transfer5. The origin and potential interconversion of these two subsets is still unclear. If they are separate subsets postnatally, it might be possible to reverse the ageing phenotype by eliminating my-HSCs in aged mice. Here we demonstrate that antibody-mediated depletion of my-HSCs in aged mice restores characteristic features of a more youthful immune system, including increasing common lymphocyte progenitors, naive T cells and B cells, while decreasing age-related markers of immune decline. Depletion of my-HSCs in aged mice improves primary and secondary adaptive immune responses to viral infection. These findings may have relevance to the understanding and intervention of diseases exacerbated or caused by dominance of the haematopoietic system by my-HSCs.

Ultrasound-responsive spherical nucleic acid against c-Myc/PD-L1 to enhance anti-tumoral macrophages in triple-negative breast cancer progression

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype because of its aggressive behavior and limited therapeutic targets. c-Myc is hyperactivated in the majority of TNBC tissues, however, it has been considered an "undruggable" target due to its disordered structure. Herein, we developed an ultrasound-responsive spherical nucleic acid (SNA) against c-Myc and PD-L1 in TNBC. It is a self-assembled and carrier-free system composed of a hydrophilic small-interfering RNA (siRNA) shell and a hydrophobic core made of a peptide nucleic acid (PNA)-based antisense oligonucleotide (ASO) and a sonosensitizer. We accomplished significant enrichment in the tumor by enhanced permeability and retention (EPR) effect, the controllable release of effective elements by ultrasound activation, and the combination of targeted therapy, immunotherapy and physiotherapy. Our study demonstrated significant anti-tumoral effects in vitro and in vivo. Mass cytometry showed an invigorated tumor microenvironment (TME) characterized by a significant alteration in the composition of tumor-associated macrophages (TAM) and decreased proportion of PD-1-positive (PD-1+) T effector cells after appropriate treatment of the ultrasound-responsive SNA (USNA). Further experiments verified that tumor-conditioned macrophages residing in the TME were transformed into the anti-tumoral population. Our finding offers a novel therapeutic strategy against the "undruggable" c-Myc, develops a new targeted therapy for c-Myc/PD-L1 and provides a treatment option for the TNBC.

In vivo single-cell high-dimensional mass cytometry analysis to track the interactions between Klebsiella pneumoniae and myeloid cells

In vivo single-cell approaches have transformed our understanding of the immune populations in tissues. Mass cytometry (CyTOF), that combines the resolution of mass spectrometry with the ability to conduct multiplexed measurements of cell molecules at the single cell resolution, has enabled to resolve the diversity of immune cell subsets, and their heterogeneous functionality. Here we assess the feasibility of taking CyTOF one step further to immuno profile cells while tracking their interactions with bacteria, a method we term Bac-CyTOF. We focus on the pathogen Klebsiella pneumoniae interrogating the pneumonia mouse model. Using Bac-CyTOF, we unveil the atlas of immune cells of mice infected with a K. pneumoniae hypervirulent strain. The atlas is characterized by a decrease in the populations of alveolar and monocyte-derived macrophages. Conversely, neutrophils, and inflammatory monocytes are characterized by an increase in the subpopulations expressing markers of less active cells such as the immune checkpoint PD-L1. These are the cells infected. We show that the type VI secretion system (T6SS) contributes to shape the lung immune landscape. The T6SS governs the interaction with monocytes/macrophages by shifting Klebsiella from alveolar macrophages to interstitial macrophages and limiting the infection of inflammatory monocytes. The lack of T6SS results in an increase of cells expressing markers of active cells, and a decrease in the subpopulations expressing PD-L1. By probing Klebsiella, and Acinetobacter baumannii strains with limited ability to survive in vivo, we uncover that a heightened recruitment of neutrophils, and relative high levels of alveolar macrophages and eosinophils and the recruitment of a characteristic subpopulation of neutrophils are features of mice clearing infections. We leverage Bac-CyTOF-generated knowledge platform to investigate the role of the DNA sensor STING in Klebsiella infections. sting-/- infected mice present features consistent with clearing the infection including the reduced levels of PD-L1. STING absence facilitates Klebsiella clearance.

Single-cell analysis of 5-aminolevulinic acid intraoperative labeling specificity for glioblastoma

OBJECTIVE

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor, and resection is a key part of the standard of care. In fluorescence-guided surgery (FGS), fluorophores differentiate tumor tissue from surrounding normal brain. The heme synthesis pathway converts 5-aminolevulinic acid (5-ALA), a fluorogenic substrate used for FGS, to fluorescent protoporphyrin IX (PpIX). The resulting fluorescence is believed to be specific to neoplastic glioma cells, but this specificity has not been examined at a single-cell level. The objective of this study was to determine the specificity with which 5-ALA labels the diversity of cell types in GBM.

METHODS

The authors performed single-cell optical phenotyping and expression sequencing-version 2 (SCOPE-seq2), a paired single-cell imaging and RNA sequencing method, of individual cells on human GBM surgical specimens with macroscopically visible PpIX fluorescence from patients who received 5-ALA prior to surgery. SCOPE-seq2 allowed the authors to simultaneously image PpIX fluorescence and unambiguously identify neoplastic cells from single-cell RNA sequencing. Experiments were also conducted in cell culture and co-culture models of glioma and in acute slice cultures from a mouse glioma model to investigate cell- and tissue-specific uptake and secretion of 5-ALA and PpIX.

RESULTS

SCOPE-seq2 analysis of human GBM surgical specimens revealed that 5-ALA treatment resulted in labeling that was not specific to neoplastic glioma cells. The cell culture further demonstrated that nonneoplastic cells could be labeled by 5-ALA directly or by PpIX secreted from surrounding neoplastic cells. Acute slice cultures from mouse glioma models showed that 5-ALA preferentially labeled GBM tumor tissue over nonneoplastic brain tissue with significant labeling in the tumor margins, and that this contrast was not due to blood-brain barrier disruption.

CONCLUSIONS

Together, these findings support the use of 5-ALA as an indicator of GBM tissue but question the main advantage of 5-ALA for specific intracellular labeling of neoplastic glioma cells in FGS. Further studies are needed to systematically compare the performance of 5-ALA to that of potential alternatives for FGS.

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 .

SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging

Spatial proteomics elucidates cellular biochemical changes with unprecedented topological level. Imaging mass cytometry (IMC) is a high-dimensional single-cell resolution platform for targeted spatial proteomics. However, the precision of subsequent clinical analysis is constrained by imaging noise and resolution. Here, we propose SpiDe-Sr, a super-resolution network embedded with a denoising module for IMC spatial resolution enhancement. SpiDe-Sr effectively resists noise and improves resolution by 4 times. We demonstrate SpiDe-Sr respectively with cells, mouse and human tissues, resulting 18.95%/27.27%/21.16% increase in peak signal-to-noise ratio and 15.95%/31.63%/15.52% increase in cell extraction accuracy. We further apply SpiDe-Sr to study the tumor microenvironment of a 20-patient clinical breast cancer cohort with 269,556 single cells, and discover the invasion of Gram-negative bacteria is positively correlated with carcinogenesis markers and negatively correlated with immunological markers. Additionally, SpiDe-Sr is also compatible with fluorescence microscopy imaging, suggesting SpiDe-Sr an alternative tool for microscopy image super-resolution.

Differences in the expression of cortex-wide neural dynamics are related to behavioral phenotype

Behavior differs across individuals, ranging from typical to atypical phenotypes.1 Understanding how differences in behavior relate to differences in neural activity is critical for developing treatments of neuropsychiatric and neurodevelopmental disorders. One hypothesis is that differences in behavior reflect individual differences in the dynamics of how information flows through the brain. In support of this, the correlation of neural activity between brain areas, termed "functional connectivity," varies across individuals2 and is disrupted in autism,3 schizophrenia,4 and depression.5 However, the changes in neural activity that underlie altered behavior and functional connectivity remain unclear. Here, we show that individual differences in the expression of different patterns of cortical neural dynamics explain variability in both functional connectivity and behavior. Using mesoscale imaging, we recorded neural activity across the dorsal cortex of behaviorally "typical" and "atypical" mice. All mice shared the same recurring cortex-wide spatiotemporal motifs of neural activity, and these motifs explained the large majority of variance in cortical activity (>75%). However, individuals differed in how frequently different motifs were expressed. These differences in motif expression explained differences in functional connectivity and behavior across both typical and atypical mice. Our results suggest that differences in behavior and functional connectivity are due to changes in the processes that select which pattern of neural activity is expressed at each moment in time.

Spatial Mass Cytometry-Based Single-Cell Imaging Reveals a Disrupted Epithelial-Immune Axis in Prurigo Nodularis

Prurigo nodularis (PN) is a chronic, inflammatory skin condition that disproportionately affects African Americans and features intensely pruritic, hyperkeratotic nodules on the extremities and trunk. PN is understudied compared with other inflammatory skin diseases, with the spatial organization of the cutaneous infiltrate in PN yet to be characterized. In this work, we employ spatial imaging mass cytometry to visualize PN lesional skin inflammation and architecture with single-cell resolution through an unbiased machine learning approach. PN lesional skin has increased expression of caspase 3, NF-kB, and phosphorylated signal transducer and activator of transcription 3 compared with healthy skin. Keratinocytes in lesional skin are subdivided into CD14+CD33+, CD11c+, CD63+, and caspase 3-positive innate subpopulations. CD14+ macrophage populations expressing phosphorylated extracellular signal-regulated kinase 1/2 correlate positively with patient-reported itch (P = .006). Hierarchical clustering reveals a cluster of patients with PN with greater atopy, increased NF-kB+ signal transducer and activator of transcription 3-positive phosphorylated extracellular signal-regulated kinase 1/2-positive monocyte-derived myeloid dendritic cells, and increased vimentin expression (P < .05). Neighborhood analysis finds interactions between CD14+ macrophages, CD3+ T cells, monocyte-derived myeloid dendritic cells, and keratinocytes expressing innate immune markers. These findings highlight phosphorylated extracellular signal-regulated kinase-positive CD14+ macrophages as contributors to itch and suggest an epithelial-immune axis in PN pathogenesis.

Exploring NK cell receptor dynamics in paediatric leukaemias: implications for immunotherapy and prognosis

Objectives

Immunotherapies targeting natural killer (NK) cell receptors have shown promise against leukaemia. Unfortunately, cancer immunosuppressive mechanisms that alter NK cell phenotype prevent such approaches from being successful. The study utilises advanced cytometry to examine how cancer immunosuppressive pathways affect NK cell phenotypic changes in clinical samples.

Methods

In this study, we conducted a high-dimensional examination of the cell surface expression of 16 NK cell receptors in paediatric patients with acute myeloid leukaemia and acute lymphoblastic leukaemia, as well as in samples of non-age matched adult peripheral blood (APB) and umbilical cord blood (UCB). An unsupervised analysis was carried out in order to identify NK cell populations present in paediatric leukaemias.

Results

We observed that leukaemia NK cells clustered together with UCB NK cells and expressed relatively higher levels of the NKG2A receptor compared to APB NK cells. In addition, CD56dimCD16+CD57- NK cells lacking NKG2A expression were mainly absent in paediatric leukaemia patients. However, CD56br NK cell populations expressing high levels of NKG2A were highly represented in paediatric leukaemia patients. NKG2A expression on leukaemia NK cells was found to be positively correlated with the expression of its ligand, suggesting that the NKG2A-HLA-E interaction may play a role in modifying NK cell responses to leukaemia cells.

Conclusion

We provide an in-depth analysis of NK cell populations in paediatric leukaemia patients. These results support the development of immunotherapies targeting immunosuppressive receptors, such as NKG2A, to enhance innate immunity against paediatric leukaemia.

Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs

Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.

Follicular lymphoma B cells exhibit heterogeneous transcriptional states with associated somatic alterations and tumor microenvironments

Follicular lymphoma (FL) is an indolent non-Hodgkin lymphoma of germinal center origin, which presents with significant biologic and clinical heterogeneity. Using RNA-seq on B cells sorted from 87 FL biopsies, combined with machine-learning approaches, we identify 3 transcriptional states that divide the biological ontology of FL B cells into inflamed, proliferative, and chromatin-modifying states, with relationship to prior GC B cell phenotypes. When integrated with whole-exome sequencing and immune profiling, we find that each state was associated with a combination of mutations in chromatin modifiers, copy-number alterations to TNFAIP3, and T follicular helper cells (Tfh) cell interactions, or primarily by a microenvironment rich in activated T cells. Altogether, these data define FL B cell transcriptional states across a large cohort of patients, contribute to our understanding of FL heterogeneity at the tumor cell level, and provide a foundation for guiding therapeutic intervention.

Circulation immune cell landscape in canonical pathogenesis of colorectal adenocarcinoma by CyTOF analysis

Current studies on the immune microenvironment of colorectal cancer (CRC) were mostly limited to the tissue level, lacking relevant studies in the peripheral blood, and failed to describe its alterations in the whole process of adenocarcinoma formation, especially of adenoma carcinogenesis. Here, we constructed a large-scale population cohort and used the CyTOF to explore the changes of various immune cell subsets in peripheral blood of CRC. We found monocytes and basophils cells were significantly higher in adenocarcinoma patients. Compared with early-stage CRC, effector CD4+T cells and naive B cells were higher in patients with lymph node metastasis, whereas the basophils were lower. We also performed random forest algorithm and found monocytes play the key role in carcinogenesis. Our study draws a peripheral blood immune cell landscape of the occurrence and development of CRC at the single-cell level and provides a reference for other researchers.

Single cell atlas of human gastric muscle immune cells and macrophage-driven changes in idiopathic gastroparesis

Gastrointestinal immune cells, particularly muscularis macrophages (MM) interact with the enteric nervous system and influence gastrointestinal motility. Here we determine the human gastric muscle immunome and its changes in patients with idiopathic gastroparesis (IG). Single cell sequencing was performed on 26,000 CD45+ cells obtained from the gastric tissue of 20 subjects. We demonstrate 11 immune cell clusters with T cells being most abundant followed by myeloid cells. The proportions of cells belonging to the 11 clusters were similar between IG and controls. However, 9/11 clusters showed 578-11,429 differentially expressed genes. In IG, MM had decreased expression of tissue-protective and microglial genes and increased the expression of monocyte trafficking and stromal activating genes. Furthermore, in IG, IL12 mediated JAK-STAT signaling involved in the activation of tissue-resident macrophages and Eph-ephrin signaling involved in monocyte chemotaxis were upregulated. Patients with IG had a greater abundance of monocyte-like cells. These data further link immune dysregulation to the pathophysiology of gastroparesis.

Differentiation marker-negative CD4+ T cells persist after yellow fever virus vaccination and contribute to durable memory

Factors that contribute to durable immunological memory remain incompletely understood. In our longitudinal analyses of CD4+ T cell responses to the yellow fever virus (YFV) vaccine by peptide-MHC tetramers, we unexpectedly found naïve phenotype virus-specific CD4+ T cells that persisted months to years after immunization. These Marker negative T cells (TMN) lacked CD95, CXCR3, CD11a, and CD49d surface protein expression, distinguishing them from previously discovered stem-cell memory T cells. Functionally, they resembled genuine naïve T cells upon in vitro stimulation. Single-cell TCR sequencing detected expanded clonotypes within the TMN subset and identified a shared repertoire with memory and effector T cells. T cells expressing TMN-associated TCRs were rare before vaccination, suggesting their expansion following vaccination. Longitudinal tracking of YFV-specific responses over the subsequent years revealed superior stability of the TMN subset and their association with the longevity of the overall population. The identification of these long-lived, antigen-experienced T cells may inform the design of durable T cell-based vaccines and engineered T cell therapies.

Application of Mass Cytometry Platforms to Solid Organ Transplantation

Transplantation serves as the cornerstone of treatment for patients with end-stage organ disease. The prevalence of complications, such as allograft rejection, infection, and malignancies, underscores the need to dissect the complex interactions of the immune system at the single-cell level. In this review, we discuss studies using mass cytometry or cytometry by time-of-flight, a cutting-edge technology enabling the characterization of immune populations and cell-to-cell interactions in granular detail. We review the application of mass cytometry in human and experimental animal studies in the context of transplantation, uncovering invaluable contributions of the tool to understanding rejection and other transplant-related complications. We discuss recent innovations that have the potential to streamline and standardize mass cytometry workflows for application to multisite clinical trials. Additionally, we introduce imaging mass cytometry, a technique that couples the power of mass cytometry with spatial context, thereby mapping cellular interactions within tissue microenvironments. The synergistic integration of mass cytometry and imaging mass cytometry data with other omics data sets and high-dimensional data platforms to further define immune dynamics is discussed. In conclusion, mass cytometry technologies, when integrated with other tools and data, shed light on the intricate landscape of the immune response in transplantation. This approach holds significant potential for enhancing patient outcomes by advancing our understanding and facilitating the development of new diagnostics and therapeutics.

Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer

Despite advances in treatment, lung cancer survival rates remain low. A better understanding of the cellular heterogeneity and interplay of cancer-associated fibroblasts (CAFs) within the tumor microenvironment will support the development of personalized therapies. We report a spatially resolved single-cell imaging mass cytometry (IMC) analysis of CAFs in a non-small cell lung cancer cohort of 1,070 patients. We identify four prognostic patient groups based on 11 CAF phenotypes with distinct spatial distributions and show that CAFs are independent prognostic factors for patient survival. The presence of tumor-like CAFs is strongly correlated with poor prognosis. In contrast, inflammatory CAFs and interferon-response CAFs are associated with inflamed tumor microenvironments and higher patient survival. High density of matrix CAFs is correlated with low immune infiltration and is negatively correlated with patient survival. In summary, our data identify phenotypic and spatial features of CAFs that are associated with patient outcome in NSCLC.

Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients

Follicular lymphoma (FL) is a generally incurable malignancy that evolves from developmentally blocked germinal center (GC) B cells. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Dynamic interactions between tumor B cells and the tumor microenvironment (TME) are hypothesized to contribute to the broad spectrum of clinical behaviors observed among FL patients. Despite the urgent need, existing clinical tools do not reliably predict disease behavior. Using a multi-modal strategy, we examined cell-intrinsic and -extrinsic factors governing progression and therapeutic outcomes in FL patients enrolled onto a prospective clinical trial. By leveraging the strengths of each platform, we identify several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk FL patients. These features include stromal desmoplasia and changes to the follicular growth pattern present 20 months before first progression and first relapse.

Excess of body weight is associated with accelerated T-cell senescence in hospitalized COVID-19 patients

BACKGROUND

Several risk factors have been involved in the poor clinical progression of coronavirus disease-19 (COVID-19), including ageing, and obesity. SARS-CoV-2 may compromise lung function through cell damage and paracrine inflammation; and obesity has been associated with premature immunosenescence, microbial translocation, and dysfunctional innate immune responses leading to poor immune response against a range of viruses and bacterial infections. Here, we have comprehensively characterized the immunosenescence, microbial translocation, and immune dysregulation established in hospitalized COVID-19 patients with different degrees of body weight.

RESULTS

Hospitalised COVID-19 patients with overweight and obesity had similarly higher plasma LPS and sCD14 levels than controls (all p < 0.01). Patients with obesity had higher leptin levels than controls. Obesity and overweight patients had similarly higher expansions of classical monocytes and immature natural killer (NK) cells (CD56+CD16-) than controls. In contrast, reduced proportions of intermediate monocytes, mature NK cells (CD56+CD16+), and NKT were found in both groups of patients than controls. As expected, COVID-19 patients had a robust expansion of plasmablasts, contrasting to lower proportions of major T-cell subsets (CD4 + and CD8+) than controls. Concerning T-cell activation, overweight and obese patients had lower proportions of CD4+CD38+ cells than controls. Contrasting changes were reported in CD25+CD127low/neg regulatory T cells, with increased and decreased proportions found in CD4+ and CD8+ T cells, respectively. There were similar proportions of T cells expressing checkpoint inhibitors across all groups. We also investigated distinct stages of T-cell differentiation (early, intermediate, and late-differentiated - TEMRA). The intermediate-differentiated CD4 + T cells and TEMRA cells (CD4+ and CD8+) were expanded in patients compared to controls. Senescent T cells can also express NK receptors (NKG2A/D), and patients had a robust expansion of CD8+CD57+NKG2A+ cells than controls. Unbiased immune profiling further confirmed the expansions of senescent T cells in COVID-19.

CONCLUSIONS

These findings suggest that dysregulated immune cells, microbial translocation, and T-cell senescence may partially explain the increased vulnerability to COVID-19 in subjects with excess of body weight.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging

The spatial localisation of immune cells within tumours are key to understand the intercellular communications that can dictate clinical outcomes. Here, we demonstrate an analysis pipeline for highly multiplexed CODEX data to phenotype and profile spatial features and interactions in NSCLC patients that subsequently received PD1 axis immunotherapy. We found that regulatory T cells (Tregs) are enriched in non-responding patients and this was consistent with their localization within stromal and peripheral tumour-margins. Proximity-based interactions between Tregs and both monocytes (p = 0.009) and CD8+ T cells (p = 0.009) were more frequently found in non-responding patients, while macrophages were more frequently located in proximity to HLADR+ tumour cells (p = 0.01) within responding patients. Cellular neighbourhoods analysis indicated that both macrophages (p = 0.003) and effector CD4+ T cells (p = 0.01) in mixed tumour neighbourhoods, as well as CD8+ T cells (p = 0.03) in HLADR+ tumour neighbourhoods were associated with favorable clinical response. Evaluation of the inferred regulatory functions between immune cells relative to the tumour suggested that macrophages exhibit an immunosuppressive phenotype against both CD4+ and CD8+ T cells, and that this association scores more highly in ICI refractory patients. These spatial patterns are associated with overall survival in addition to ICI response and may thus indicate features for the functional understanding of the tumour microenvironment.

CDSKNNXMBD: a novel clustering framework for large-scale single-cell data based on a stable graph structure

BACKGROUND

Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency.

METHODS

We propose CDSKNNXMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure.

RESULTS

We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods.

CONCLUSIONS

The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.

A proteomic atlas of kidney amyloidosis provides insights into disease pathogenesis

The mechanisms of tissue damage in kidney amyloidosis are not well described. To investigate this further, we used laser microdissection-mass spectrometry to identify proteins deposited in amyloid plaques (expanded proteome) and proteins overexpressed in plaques compared to controls (plaque-specific proteome). This study encompassed 2650 cases of amyloidosis due to light chain (AL), heavy chain (AH), leukocyte chemotactic factor-2-type (ALECT2), secondary (AA), fibrinogen (AFib), apo AIV (AApoAIV), apo CII (AApoCII) and 14 normal/disease controls. We found that AFib, AA, and AApoCII have the most distinct proteomes predominantly driven by increased complement pathway proteins. Clustering of cases based on the expanded proteome identified two ALECT2 and seven AL subtypes. The main differences within the AL and ALECT2 subtypes were driven by complement proteins and, for AL only, 14-3-3 family proteins (a family of structurally similar phospho-binding proteins that regulate major cellular functions) widely implicated in kidney tissue dysfunction. The kidney AL plaque-specific proteome consisted of 24 proteins, including those implicated in kidney damage (α1 antitrypsin and heat shock protein β1). Hierarchical clustering of AL cases based on their plaque-specific proteome identified four clusters, of which one was associated with improved kidney survival and was characterized by higher overall proteomic content and 14-3-3 proteins but lower levels of light chains and most signature proteins. Thus, our results suggest that there is significant heterogeneity across and within amyloid types, driven predominantly by complement proteins, and that the plaque protein burden does not correlate with amyloid toxicity.

Reversing immunosuppression in the tumor microenvironment of fibrolamellar carcinoma via PD-1 and IL-10 blockade

Fibrolamellar carcinoma (FLC) is a rare liver tumor driven by the DNAJ-PKAc fusion protein that affects healthy young patients. Little is known about the immune response to FLC, limiting rational design of immunotherapy. Multiplex immunohistochemistry and gene expression profiling were performed to characterize the FLC tumor immune microenvironment and adjacent non-tumor liver (NTL). Flow cytometry and T cell receptor (TCR) sequencing were performed to determine the phenotype of tumor-infiltrating immune cells and the extent of T cell clonal expansion. Fresh human FLC tumor slice cultures (TSCs) were treated with antibodies blocking programmed cell death protein-1 (PD-1) and interleukin-10 (IL-10), with results measured by cleaved caspase-3 immunohistochemistry. Immune cells were concentrated in fibrous stromal bands, rather than in the carcinoma cell compartment. In FLC, T cells demonstrated decreased activation and regulatory T cells in FLC had more frequent expression of PD-1 and CTLA-4 than in NTL. Furthermore, T cells had relatively low levels of clonal expansion despite high TCR conservation across individuals. Combination PD-1 and IL-10 blockade signficantly increased cell death in human FLC TSCs. Immunosuppresion in the FLC tumor microenvironment is characterized by T cell exclusion and exhaustion, which may be reversible with combination immunotherapy.

Autoimmunity-associated allele of tyrosine phosphatase gene PTPN22 enhances anti-viral immunity

The 1858C>T allele of the tyrosine phosphatase PTPN22 is present in 5-10% of the North American population and is strongly associated with numerous autoimmune diseases. Although research has been done to define how this allele potentiates autoimmunity, the influence PTPN22 and its pro-autoimmune allele has in anti-viral immunity remains poorly defined. Here, we use single cell RNA-sequencing and functional studies to interrogate the impact of this pro-autoimmune allele on anti-viral immunity during Lymphocytic Choriomeningitis Virus clone 13 (LCMV-cl13) infection. Mice homozygous for this allele (PEP-619WW) clear the LCMV-cl13 virus whereas wildtype (PEP-WT) mice cannot. This is associated with enhanced anti-viral CD4 T cell responses and a more immunostimulatory CD8α- cDC phenotype. Adoptive transfer studies demonstrated that PEP-619WW enhanced anti-viral CD4 T cell function through virus-specific CD4 T cell intrinsic and extrinsic mechanisms. Taken together, our data show that the pro-autoimmune allele of Ptpn22 drives a beneficial anti-viral immune response thereby preventing what is normally a chronic virus infection.

Intestinal stroma guides monocyte differentiation to macrophages through GM-CSF

Stromal cells support epithelial cell and immune cell homeostasis and play an important role in inflammatory bowel disease (IBD) pathogenesis. Here, we quantify the stromal response to inflammation in pediatric IBD and reveal subset-specific inflammatory responses across colon segments and intestinal layers. Using data from a murine dynamic gut injury model and human ex vivo transcriptomic, protein and spatial analyses, we report that PDGFRA+CD142-/low fibroblasts and monocytes/macrophages co-localize in the intestine. In primary human fibroblast-monocyte co-cultures, intestinal PDGFRA+CD142-/low fibroblasts foster monocyte transition to CCR2+CD206+ macrophages through granulocyte-macrophage colony-stimulating factor (GM-CSF). Monocyte-derived CCR2+CD206+ cells from co-cultures have a phenotype similar to intestinal CCR2+CD206+ macrophages from newly diagnosed pediatric IBD patients, with high levels of PD-L1 and low levels of GM-CSF receptor. The study describes subset-specific changes in stromal responses to inflammation and suggests that the intestinal stroma guides intestinal macrophage differentiation.

Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer

Immune system control is a major hurdle that cancer evolution must circumvent. The relative timing and evolutionary dynamics of subclones that have escaped immune control remain incompletely characterized, and how immune-mediated selection shapes the epigenome has received little attention. Here, we infer the genome- and epigenome-driven evolutionary dynamics of tumour-immune coevolution within primary colorectal cancers (CRCs). We utilise our existing CRC multi-region multi-omic dataset that we supplement with high-resolution spatially-resolved neoantigen sequencing data and highly multiplexed imaging of the tumour microenvironment (TME). Analysis of somatic chromatin accessibility alterations (SCAAs) reveals frequent somatic loss of accessibility at antigen presenting genes, and that SCAAs contribute to silencing of neoantigens. We observe that strong immune escape and exclusion occur at the outset of CRC formation, and that within tumours, including at the microscopic level of individual tumour glands, additional immune escape alterations have negligible consequences for the immunophenotype of cancer cells. Further minor immuno-editing occurs during local invasion and is associated with TME reorganisation, but that evolutionary bottleneck is relatively weak. Collectively, we show that immune evasion in CRC follows a "Big Bang" evolutionary pattern, whereby genetic, epigenetic and TME-driven immune evasion acquired by the time of transformation defines subsequent cancer-immune evolution.

Innate and adaptive immune cell interaction drives inflammasome activation and hepatocyte apoptosis in murine liver injury from immune checkpoint inhibitors

Immune checkpoints (CTLA4 & PD-1) are inhibitory pathways that block aberrant immune activity and maintain self-tolerance. Tumors co-opt these checkpoints to avoid immune destruction. Immune checkpoint inhibitors (ICIs) activate immune cells and restore their tumoricidal potential, making them highly efficacious cancer therapies. However, immunotolerant organs such as the liver depend on these tolerogenic mechanisms, and their disruption with ICI use can trigger the unintended side effect of hepatotoxicity termed immune-mediated liver injury from ICIs (ILICI). Learning how to uncouple ILICI from ICI anti-tumor activity is of paramount clinical importance. We developed a murine model to recapitulate human ILICI using CTLA4+/- mice treated with either combined anti-CTLA4 + anti-PDL1 or IgG1 + IgG2. We tested two forms of antisense oligonucleotides to knockdown caspase-3 in a total liver (parenchymal and non-parenchymal cells) or in a hepatocyte-specific manner. We also employed imaging mass cytometry (IMC), a powerful multiplex modality for immunophenotyping and cell interaction analysis in our model. ICI-treated mice had significant evidence of liver injury. We detected cleaved caspase-3 (cC3), indicating apoptosis was occurring, as well as Nod-like receptor protein 3 (NLRP3) inflammasome activation, but no necroptosis. Total liver knockdown of caspase-3 worsened liver injury, and induced further inflammasome activation, and Gasdermin-D-mediated pyroptosis. Hepatocyte-specific knockdown of caspase-3 reduced liver injury and NLRP3 inflammasome activation. IMC-generated single-cell data for 77,692 cells was used to identify 22 unique phenotypic clusters. Spatial analysis revealed that cC3+ hepatocytes had significantly closer interactions with macrophages, Kupffer cells, and NLRP3hi myeloid cells than other cell types. We also observed zones of three-way interaction between cC3+ hepatocytes, CD8 + T-cells, and macrophages. Our work is the first to identify hepatocyte apoptosis and NLRP3 inflammasome activation as drivers of ILICI. Furthermore, we report that the interplay between adaptive and innate immune cells is critical to hepatocyte apoptosis and ILICI.

Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy

Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.

Lineage-specific intolerance to oncogenic drivers restricts histological transformation

Lung adenocarcinoma (LUAD) and small cell lung cancer (SCLC) are thought to originate from different epithelial cell types in the lung. Intriguingly, LUAD can histologically transform into SCLC after treatment with targeted therapies. In this study, we designed models to follow the conversion of LUAD to SCLC and found that the barrier to histological transformation converges on tolerance to Myc, which we implicate as a lineage-specific driver of the pulmonary neuroendocrine cell. Histological transformations are frequently accompanied by activation of the Akt pathway. Manipulating this pathway permitted tolerance to Myc as an oncogenic driver, producing rare, stem-like cells that transcriptionally resemble the pulmonary basal lineage. These findings suggest that histological transformation may require the plasticity inherent to the basal stem cell, enabling tolerance to previously incompatible oncogenic driver programs.

QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport

Single-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The data's innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes

It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell-cell communication patterns across spatial scales.

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Integrative single-cell expression and functional studies unravels a sensitization to cytarabine-based chemotherapy through HIF pathway inhibition in AML leukemia stem cells

Relapse remains a major challenge in the clinical management of acute myeloid leukemia (AML) and is driven by rare therapy-resistant leukemia stem cells (LSCs) that reside in specific bone marrow niches. Hypoxia signaling maintains cells in a quiescent and metabolically relaxed state, desensitizing them to chemotherapy. This suggests the hypothesis that hypoxia contributes to the chemoresistance of AML-LSCs and may represent a therapeutic target to sensitize AML-LSCs to chemotherapy. Here, we identify HIFhigh and HIFlow specific AML subgroups (inv(16)/t(8;21) and MLLr, respectively) and provide a comprehensive single-cell expression atlas of 119,000 AML cells and AML-LSCs in paired diagnostic-relapse samples from these molecular subgroups. The HIF/hypoxia pathway signature is attenuated in AML-LSCs compared with more differentiated AML cells but is more expressed than in healthy hematopoietic cells. Importantly, chemical inhibition of HIF cooperates with standard-of-care chemotherapy to impair AML growth and to substantially eliminate AML-LSCs in vitro and in vivo. These findings support the HIF pathway in the stem cell-driven drug resistance of AML and unravel avenues for combinatorial targeted and chemotherapy-based approaches to specifically eliminate AML-LSCs.

Ex Vivo Expanded Cord Blood Natural Killer Cells Combined with Rituximab and High-Dose Chemotherapy and Autologous Stem Cell Transplantation for B Cell Non-Hodgkin Lymphoma

Relapse is the major cause of failure of high-dose chemotherapy (HDC) with autologous stem cell transplantation (ASCT) for B cell non-Hodgkin lymphomas (B-NHL). Improvement strategies include use in combination with effective immunotherapies. We hypothesized that the combination of rituximab/HDC/ASCT with expanded cord blood (CB)-derived natural killer (NK) cells is safe and active in B-NHL. Patients with B-NHL age 15 to 70 years and appropriate ASCT candidates were eligible for the study. The CB units were selected without considering HLA match with the recipient. The CB NK cells were expanded from day -19 to day -5. Treatment included rituximab on days -13 and -7, BEAM (carmustine/etoposide/cytarabine/melphalan) on days -13 to -7, lenalidomide on days -7 to -2, CB NK infusion (108/kg) on day -5, and ASCT (day 0). The primary endpoint was 30-day treatment-related mortality (TRM); secondary endpoints included relapse-free survival (RFS), overall survival (OS), and persistence of CB NK cells. We enrolled 20 patients. CB NK cells were expanded a median of 1552-fold with >98% purity and >96% viability. We saw no adverse events attributable to the CB NK cells and 0% 30-day TRM. At median follow-up of 47 months, the RFS and OS rates were 53% and 74%, respectively. CB NK cells were detectable in blood for 2 weeks, independent of HLA-mismatch status. CD16 expression in donor NK cells was correlated favorably with outcome, and homozygosity for the high-affinity CD16 variant (158 V/V) in CB, but not recipient, NK cells was correlated with better outcomes. Our data indicate that the combination of expanded and highly purified CB-derived NK cells with HDC/ASCT for B-NHL is safe. CD16 expression in donor NK cells, particularly if homozygous for the high-affinity CD16 variant, was correlated with better outcomes.

Stromal localization of inactive CD8+ T cells in metastatic mismatch repair deficient colorectal cancer

BACKGROUND

The determinants of metastasis in mismatch repair deficiency with high levels of microsatellite instability (MSI-H) in colorectal cancer (CRC) are poorly understood. Here, we hypothesized that distinct immune and stromal microenvironments in primary tumors may discriminate between non-metastatic MSI-H CRC and metastatic MSI-H CRC.

METHODS

We profiled 46,727 single cells using high-plex imaging mass cytometry and analyzed both differential cell type abundance, and spatial distribution of fibroblasts and immune cells in primary CRC tumors with or without metastatic capacity. We validated our findings in a second independent cohort using immunohistochemistry.

RESULTS

High-plex imaging mass cytometry and hierarchical clustering based on microenvironmental markers separated primary MSI-H CRC tumors with and without metastatic capacity. Primary tumors with metastatic capacity displayed a high stromal content and low influx of CD8+ T cells, which expressed significantly lower levels of markers reflecting proliferation (Ki67) and antigen-experience (CD45RO) compared to CD8+ T cells in non-metastatic tumors. CD8+ T cells showed intra-epithelial localization in non-metastatic tumors, but stromal localization in metastatic tumors, which was validated in a second cohort.

CONCLUSION

We conclude that localization of phenotypically distinct CD8+ T cells within stroma may predict metastasis formation in MSI-H CRC.

scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision

MOTIVATION

Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been developed, the low signal-to-noise-ratio single-cell RNA-sequencing data that suffers from batch effects and dropout still poses obstacles in discovering grouped patterns for cell types by unsupervised learning and its alternative-semi-supervised learning that utilizes a few labeled cells as guidance for cell-type annotation.

RESULTS

We propose a robust cell-type annotation method scSemiGCN based on graph convolutional networks. Built upon a denoised network structure that characterizes reliable cell-to-cell connections, scSemiGCN generates pseudo labels for unannotated cells. Then supervised contrastive learning follows to refine the noisy single-cell data. Finally, message passing with the refined features over the denoised network structure is conducted for semi-supervised cell-type annotation. Comparison over several datasets with six methods under extremely limited supervision validates the effectiveness and efficiency of scSemiGCN for cell-type annotation.

AVAILABILITY AND IMPLEMENTATION

Implementation of scSemiGCN is available at https://github.com/Jane9898/scSemiGCN.

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS-TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28-marker panel for single-cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance.

Small Cajal Body-Specific RNA12 Promotes Carcinogenesis through Modulating Extracellular Matrix Signaling in Bladder Cancer

Background: Small Cajal body-specific RNAs (scaRNAs) are a specific subset of small nucleolar RNAs (snoRNAs) that have recently emerged as pivotal contributors in diverse physiological and pathological processes. However, their defined roles in carcinogenesis remain largely elusive. This study aims to explore the potential function and mechanism of SCARNA12 in bladder cancer (BLCA) and to provide a theoretical basis for further investigations into the biological functionalities of scaRNAs. Materials and Methods: TCGA, GEO and GTEx data sets were used to analyze the expression of SCARNA12 and its clinicopathological significance in BLCA. Quantitative real-time PCR (qPCR) and in situ hybridization were applied to validate the expression of SCARNA12 in both BLCA cell lines and tissues. RNA sequencing (RNA-seq) combined with bioinformatics analyses were conducted to reveal the changes in gene expression patterns and functional pathways in BLCA patients with different expressions of SCARNA12 and T24 cell lines upon SCARNA12 knockdown. Single-cell mass cytometry (CyTOF) was then used to evaluate the tumor-related cell cluster affected by SCARNA12. Moreover, SCARNA12 was stably knocked down in T24 and UMUC3 cell lines by lentivirus-mediated CRISPR/Cas9 approach. The biological effects of SCARNA12 on the proliferation, clonogenic, migration, invasion, cell apoptosis, cell cycle, and tumor growth were assessed by in vitro MTT, colony formation, wound healing, transwell, flow cytometry assays, and in vivo nude mice xenograft models, respectively. Finally, a chromatin isolation by RNA purification (ChIRP) experiment was further conducted to delineate the potential mechanisms of SCARNA12 in BLCA. Results: The expression of SCARNA12 was significantly up-regulated in both BLCA tissues and cell lines. RNA-seq data elucidated that SCARAN12 may play a potential role in cell adhesion and extracellular matrix (ECM) related signaling pathways. CyTOF results further showed that an ECM-related cell cluster with vimentin+, CD13+, CD44+, and CD47+ was enriched in BLCA patients with high SCARNA12 expression. Additionally, SCARNA12 knockdown significantly inhibited the proliferation, colony formation, migration, and invasion abilities in T24 and UMUC3 cell lines. SCARNA12 knockdown prompted cell arrest in the G0/G1 and G2/M phase and promoted apoptosis in T24 and UMUC3 cell lines. Furthermore, SCARNA12 knockdown could suppress the in vivo tumor growth in nude mice. A ChIRP experiment further suggested that SCARNA12 may combine transcription factors H2AFZ to modulate the transcription program and then affect BLCA progression. Conclusions: Our study is the first to propose aberrant alteration of SCARNA12 and elucidate its potential oncogenic roles in BLCA via the modulation of ECM signaling. The interaction of SCARNA12 with the transcriptional factor H2AFZ emerges as a key contributor to the carcinogenesis and progression of BLCA. These findings suggest SCARNA12 may serve as a diagnostic biomarker and potential therapeutic target for the treatment of BLCA.

Enhanced detection of antigen-specific T cells by a multiplexed AIM assay

Broadly applicable methods to identify and characterize antigen-specific CD4+ and CD8+ T cells are key to immunology research, including studies of vaccine responses and immunity to infectious diseases. We developed a multiplexed activation-induced marker (AIM) assay that presents several advantages compared to single pairs of AIMs. The simultaneous measurement of four AIMs (CD69, 4-1BB, OX40, and CD40L) creates six AIM pairs that define CD4+ T cell populations with partial and variable overlap. When combined in an AND/OR Boolean gating strategy for analysis, this approach enhances CD4+ T cell detection compared to any single AIM pair, while CD8+ T cells are dominated by CD69/4-1BB co-expression. Supervised and unsupervised clustering analyses show differential expression of the AIMs in defined T helper lineages and that multiplexing mitigates phenotypic biases. Paired and unpaired comparisons of responses to infections (HIV and cytomegalovirus [CMV]) and vaccination (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) validate the robustness and versatility of the method.

Persistent complement dysregulation with signs of thromboinflammation in active Long Covid

Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid. Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte-platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.

A novel mass cytometry protocol optimized for immunophenotyping of low-frequency antigen-specific T cells

Understanding antigen-specific T-cell responses, for example, following virus infections or allergen exposure, is of high relevance for the development of vaccines and therapeutics. We aimed on optimizing immunophenotyping of T cells after antigen stimulation by improving staining procedures for flow and mass cytometry. Our method can be used for primary cells of both mouse and human origin for the detection of low-frequency T-cell response using a dual-barcoding system for individual samples and conditions. First, live-cell barcoding was performed using anti-CD45 antibodies prior to an in vitro T-cell stimulation assay. Second, to discriminate between stimulation conditions and prevent cell loss, sample barcoding was combined with a commercial barcoding solution. This dual-barcoding approach is cell sparing and, therefore, particularly relevant for samples with low cell numbers. To further reduce cell loss and to increase debarcoding efficiency of multiplexed samples, we combined our dual-barcoding approach with a new centrifugation-free washing system by laminar flow (Curiox™). Finally, to demonstrate the benefits of our established protocol, we assayed virus-specific T-cell response in SARS-CoV-2-vaccinated and SARS-CoV-2-infected patients and compared with healthy non-exposed individuals by a high-parameter CyTOF analysis. We could reveal a heterogeneity of phenotypes among responding CD4, CD8, and gd-T cells following antigen-specific stimulations. Our protocol allows to assay antigen-specific responses of minute populations of T cells to virus-derived peptides, allergens, or other antigens from the same donor sample, in order to investigate qualitative and quantitative differences.

Spherical harmonics analysis reveals cell shape-fate relationships in zebrafish lateral line neuromasts

Cell shape is a powerful readout of cell state, fate and function. We describe a custom workflow to perform semi-automated, 3D cell and nucleus segmentation, and spherical harmonics and principal components analysis to distill cell and nuclear shape variation into discrete biologically meaningful parameters. We apply these methods to analyze shape in the neuromast cells of the zebrafish lateral line system, finding that shapes vary with cell location and identity. The distinction between hair cells and support cells accounted for much of the variation, which allowed us to train classifiers to predict cell identity from shape features. Using transgenic markers for support cell subpopulations, we found that subtypes had different shapes from each other. To investigate how loss of a neuromast cell type altered cell shape distributions, we examined atoh1a mutants that lack hair cells. We found that mutant neuromasts lacked the cell shape phenotype associated with hair cells, but did not exhibit a mutant-specific cell shape. Our results demonstrate the utility of using 3D cell shape features to characterize, compare and classify cells in a living developing organism.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Epigenetic reprogramming shapes the cellular landscape of schwannoma

Mechanisms specifying cancer cell states and response to therapy are incompletely understood. Here we show epigenetic reprogramming shapes the cellular landscape of schwannomas, the most common tumors of the peripheral nervous system. We find schwannomas are comprised of 2 molecular groups that are distinguished by activation of neural crest or nerve injury pathways that specify tumor cell states and the architecture of the tumor immune microenvironment. Moreover, we find radiotherapy is sufficient for interconversion of neural crest schwannomas to immune-enriched schwannomas through epigenetic and metabolic reprogramming. To define mechanisms underlying schwannoma groups, we develop a technique for simultaneous interrogation of chromatin accessibility and gene expression coupled with genetic and therapeutic perturbations in single-nuclei. Our results elucidate a framework for understanding epigenetic drivers of tumor evolution and establish a paradigm of epigenetic and metabolic reprograming of cancer cells that shapes the immune microenvironment in response to radiotherapy.