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

Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors

BACKGROUND

Tertiary lymphoid structures (TLSs) are dense accumulations of lymphocytes in inflamed peripheral tissues, including cancer, and are associated with improved survival and response to immunotherapy in various solid tumors. Histological TLS quantification has been proposed as a novel predictive and prognostic biomarker, but lack of standardized methods of TLS characterization hampers assessment of TLS densities across different patients, diseases, and clinical centers.

METHODS

We introduce an approach based on HookNet-TLS, a multi-resolution deep learning model, for automated and unbiased TLS quantification and identification of germinal centers in routine hematoxylin and eosin stained digital pathology slides. We developed HookNet-TLS using n = 1019 manually annotated TCGA slides from clear cell renal cell carcinoma, muscle-invasive bladder cancer, and lung squamous cell carcinoma.

RESULTS

Here we show that HookNet-TLS automates TLS quantification across multiple cancer types achieving human-level performance and demonstrates prognostic associations similar to visual assessment.

CONCLUSIONS

HookNet-TLS has the potential to be used as a tool for objective quantification of TLS in routine H&E digital pathology slides. We make HookNet-TLS publicly available to promote its use in research.

A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma

To better understand intrinsic resistance to immune checkpoint blockade (ICB), we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem and studied its evolution under ICB. Using single-cell, spatial multi-omics, we showed that the tumor microenvironment promotes the emergence of a complex melanoma transcriptomic landscape. Melanoma cells harboring a mesenchymal-like (MES) state, a population known to confer resistance to targeted therapy, were significantly enriched in early on-treatment biopsies from non-responders to ICB. TCF4 serves as the hub of this landscape by being a master regulator of the MES signature and a suppressor of the melanocytic and antigen presentation transcriptional programs. Targeting TCF4 genetically or pharmacologically, using a bromodomain inhibitor, increased immunogenicity and sensitivity of MES cells to ICB and targeted therapy. We thereby uncovered a TCF4-dependent regulatory network that orchestrates multiple transcriptional programs and contributes to resistance to both targeted therapy and ICB in melanoma.

Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets

BACKGROUND

To analyze the large volume of data generated by single-cell technologies and to identify cellular correlates of particular clinical or experimental outcomes, differential abundance analyses are often applied. These algorithms identify subgroups of cells whose abundances change significantly in response to disease progression, or to an experimental perturbation. Despite the effectiveness of differential abundance analyses in identifying critical cell-states, there is currently no systematic benchmarking study to compare their applicability, usefulness, and accuracy in practice across single-cell modalities.

RESULTS

Here, we perform a comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-art differential abundance testing methods. We benchmarked six single-cell testing methods on several practical tasks, using both synthetic and real single-cell datasets. The tasks evaluated include effectiveness in identifying true differentially abundant subpopulations, accuracy in the adequate handling of batch effects, runtime efficiency, and hyperparameter usability and robustness. Based on various evaluation results, this paper gives dataset-specific suggestions for the practical use of differential abundance testing approaches.

CONCLUSIONS

Based on our benchmarking study, we provide a set of recommendations for the optimal usage of single-cell DA testing methods in practice, particularly with respect to factors such as the presence of technical noise (for example batch effects), dataset size, and hyperparameter sensitivity.

Pathway analysis through mutual information

MOTIVATION

In pathway analysis, we aim to establish a connection between the activity of a particular biological pathway and a difference in phenotype. There are many available methods to perform pathway analysis, many of them rely on an upstream differential expression analysis, and many model the relations between the abundances of the analytes in a pathway as linear relationships.

RESULTS

Here, we propose a new method for pathway analysis, MIPath, that relies on information theoretical principles and, therefore, does not model the association between pathway activity and phenotype, resulting in relatively few assumptions. For this, we construct a graph of the data points for each pathway using a nearest-neighbor approach and score the association between the structure of this graph and the phenotype of these same samples using Mutual Information while adjusting for the effects of random chance in each score. The initial nearest neighbor approach evades individual gene-level comparisons, hence making the method scalable and less vulnerable to missing values. These properties make our method particularly useful for single-cell data. We benchmarked our method on several single-cell datasets, comparing it to established and new methods, and found that it produces robust, reproducible, and meaningful scores.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/statisticalbiotechnology/mipath, or through Python Package Index as "mipathway."

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images

MOTIVATION

Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks a unified approach to evaluating segmentation accuracy.

RESULTS

In this article, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations.

AVAILABILITY AND IMPLEMENTATION

Source code is available on Github at: https://github.com/Roth-Lab/ESQmodel.

Conditional NKT Cell Depletion in Mice Reveals a Negative Feedback Loop That Regulates CTL Cross-Priming

NKT cells are unconventional T cells whose biological role is incompletely understood. Similar to TH cells, activated NKT cells can cause dendritic cell (DC) maturation, which is required for effective CTL responses. However, it is unclear whether and how NKT cells affect CTLs downstream of the DC maturation phase. This is partially due to the lack of techniques to conditionally deplete NKT cells in vivo. To overcome this problem, we have developed two approaches for this purpose in mice: the first is based on mixed bone marrow chimeras where Jα18 knockout and depletable CD90 congenic bone marrow is combined, and the second used PLZFCre × iDTR bone marrow chimeras, which target innate-like T cells. Using these tools, we found that NKT cell depletion at 20 h, that is, after initial DC activation, did not render CTLs helpless, as CD40L signaling by non-NKT cells sufficed. Instead, NKT cell depletion even augmented CD8 T cell expansion and cytotoxicity by mechanisms distinct from reduced STAT6 signaling. These findings revealed a negative feedback loop by which NKT cells control CTL cross-priming downstream of DC maturation. The techniques described in this study expand the toolbox to study NKT cells and other unconventional T cell subsets in vivo and uncovered a hidden immunoregulatory mechanism.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration

Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states, or spatial relationships compared to reference data from single-cell suspension technologies. To this end we have developed the "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood analysis. We found that single-cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing phenograph in terms of cell type identification. We also found that neighborhood analysis was influenced by the method used for finding neighboring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly, OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology

Ocular lens development entails epithelial to fiber cell differentiation, defects in which cause congenital cataracts. We report the first single-cell multiomic atlas of lens development, leveraging snRNA-seq, snATAC-seq and CUT&RUN-seq to discover previously unreported mechanisms of cell fate determination and cataract-linked regulatory networks. A comprehensive profile of cis- and trans-regulatory interactions, including for the cataract-linked transcription factor MAF, is established across a temporal trajectory of fiber cell differentiation. Furthermore, we identify an epigenetic paradigm of cellular differentiation, defined by progressive loss of the H3K27 methylation writer Polycomb repressive complex 2 (PRC2). PRC2 localizes to heterochromatin domains across master-regulator transcription factor gene bodies, suggesting it safeguards epithelial cell fate. Moreover, we demonstrate that FGF hyper-stimulation in vivo leads to MAF network activation and the emergence of novel lens cell states. Collectively, these data depict a comprehensive portrait of lens fiber cell differentiation, while defining regulatory effectors of cell identity and cataract formation.

Antigen perception in T cells by long-term Erk and NFAT signaling dynamics

Immune system threat detection hinges on T cells' ability to perceive varying peptide-major histocompatibility complex (pMHC) antigens. As the Erk and NFAT pathways link T cell receptor engagement to gene regulation, their signaling dynamics may convey information about pMHC inputs. To test this idea, we developed a dual reporter mouse strain and a quantitative imaging assay that, together, enable simultaneous monitoring of Erk and NFAT dynamics in live T cells over day-long timescales as they respond to varying pMHC inputs. Both pathways initially activate uniformly across various pMHC inputs but diverge only over longer (9+ h) timescales, enabling independent encoding of pMHC affinity and dose. These late signaling dynamics are decoded via multiple temporal and combinatorial mechanisms to generate pMHC-specific transcriptional responses. Our findings underscore the importance of long timescale signaling dynamics in antigen perception and establish a framework for understanding T cell responses under diverse contexts.

Single cell proteomics characterization of bone marrow hematopoiesis with distinct Ras pathway lesions

Normal hematopoiesis requires constant prolific production of different blood cell lineages by multipotent hematopoietic stem cells (HSC). Stem- and progenitor- cells need to balance dormancy with proliferation. How genetic alterations impact frequency, lineage potential, and metabolism of HSC is largely unknown. Here, we compared induced expression of KRAS G12D or RasGRP1 to normal hematopoiesis. At low-resolution, both Ras pathway lesions result in skewing towards myeloid lineages. Single-cell resolution CyTOF proteomics unmasked an expansion of HSC- and progenitor- compartments for RasGRP1, contrasted by a depletion for KRAS G12D . SCENITH™ quantitates protein synthesis with single-cell precision and corroborated that immature cells display low metabolic SCENITH™ rates. Both RasGRP1 and KRAS G12D elevated mean SCENITH™ signals in immature cells. However, RasGRP1-overexpressing stem cells retain a metabolically quiescent cell-fraction, whereas this fraction diminishes for KRAS G12D . Our temporal single cell proteomics and metabolomics datasets provide a resource of mechanistic insights into altered hematopoiesis at single cell resolution.

A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets

Clustering analysis is widely used to group objects by similarity, but for complex datasets such as those produced by single-cell analysis, the currently available clustering methods are limited by accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method with hyperparameter randomization that outperforms other methods across a broad range of single-cell and synthetic datasets, without the need for manual hyperparameter selection. In addition to hard cluster labels, it also outputs soft cluster memberships to characterize continuum-like regions and per cell overlap scores to quantify the uncertainty in cluster assignment. We demonstrate the improved clustering interpretability from these features by tracing the intermediate stages between handwritten digits in the MNIST dataset, and between tanycyte subpopulations in the hypothalamus. This approach improves the quality of clustering and subsequent downstream analyses for single-cell datasets, and may also prove useful in other fields of data analysis.

Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data

BACKGROUND

Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing.

METHODS

We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method.

RESULTS

The neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets.

CONCLUSIONS

This work provides an approach to bridge single-cell level information toward population-level survival prediction.

Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling

Current treatment selection for acute myeloid leukemia (AML) patients depends on risk stratification based on cytogenetic and genomic markers. However, the forecasting accuracy of treatment response remains modest, with most patients receiving intensive chemotherapy. Recently, ex vivo drug screening has gained traction in personalized treatment selection and as a tool for mapping patient groups based on relevant cancer dependencies. Here, we systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can assess risk for individual AML patients and may guide clinical decision-making.

Epitranscriptional regulation of TGF-β pseudoreceptor BAMBI by m6A/YTHDF2 drives extrinsic radioresistance

Activation of TGF-β signaling serves as an extrinsic resistance mechanism that limits the potential for radiotherapy. Bone morphogenetic protein and activin membrane-bound inhibitor (BAMBI) antagonizes TGF-β signaling and is implicated in cancer progression. However, the molecular mechanisms of BAMBI regulation in immune cells and its impact on antitumor immunity after radiation have not been established. Here, we show that ionizing radiation (IR) specifically reduces BAMBI expression in immunosuppressive myeloid-derived suppressor cells (MDSCs) in both murine models and humans. Mechanistically, YTH N6-methyladenosine RNA-binding protein F2 (YTHDF2) directly binds and degrades Bambi transcripts in an N6-methyladenosine-dependent (m6A-dependent) manner, and this relies on NF-κB signaling. BAMBI suppresses the tumor-infiltrating capacity and suppression function of MDSCs via inhibiting TGF-β signaling. Adeno-associated viral delivery of Bambi (AAV-Bambi) to the tumor microenvironment boosts the antitumor effects of radiotherapy and radioimmunotherapy combinations. Intriguingly, combination of AAV-Bambi and IR not only improves local tumor control, but also suppresses distant metastasis, further supporting its clinical translation potential. Our findings uncover a surprising role of BAMBI in myeloid cells, unveiling a potential therapeutic strategy for overcoming extrinsic radioresistance.

Comprehensive Single-Cell Immune Profiling Defines the Patient Multiple Myeloma Microenvironment Following Oncolytic Virus Therapy in a Phase Ib Trial

PURPOSE

Our preclinical studies showed that the oncolytic reovirus formulation pelareorep (PELA) has significant immunomodulatory anti-myeloma activity. We conducted an investigator-initiated clinical trial to evaluate PELA in combination with dexamethasone (Dex) and bortezomib (BZ) and define the tumor immune microenvironment (TiME) in patients with multiple myeloma treated with this regimen.

PATIENTS AND METHODS

Patients with relapsed/refractory multiple myeloma (n = 14) were enrolled in a phase Ib clinical trial (ClinicalTrials.gov: NCT02514382) of three escalating PELA doses administered on Days 1, 2, 8, 9, 15, and 16. Patients received 40 mg Dex and 1.5 mg/m2 BZ on Days 1, 8, and 15. Cycles were repeated every 28 days. Pre- and posttreatment bone marrow specimens (IHC, n = 9; imaging mass cytometry, n = 6) and peripheral blood samples were collected for analysis (flow cytometry, n = 5; T-cell receptor clonality, n = 7; cytokine assay, n = 7).

RESULTS

PELA/BZ/Dex was well-tolerated in all patients. Treatment-emergent toxicities were transient, and no dose-limiting toxicities occurred. Six (55%) of 11 response-evaluable patients showed decreased paraprotein. Treatment increased T and natural killer cell activation, inflammatory cytokine release, and programmed death-ligand 1 expression in bone marrow. Compared with nonresponders, responders had higher reovirus protein levels, increased cytotoxic T-cell infiltration posttreatment, cytotoxic T cells in significantly closer proximity to multiple myeloma cells, and larger populations of a novel immune-primed multiple myeloma phenotype (CD138+ IDO1+HLA-ABCHigh), indicating immunomodulation.

CONCLUSIONS

PELA/BZ/Dex is well-tolerated and associated with anti-multiple myeloma activity in a subset of responding patients, characterized by immune reprogramming and TiME changes, warranting further investigation of PELA as an immunomodulator.

Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data

Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC = 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4+ memory T cell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development.

Development of a gene edited next-generation hematopoietic cell transplant to enable acute myeloid leukemia treatment by solving off-tumor toxicity

Immunotherapy of acute myeloid leukemia (AML) has been challenging because the lack of tumor-specific antigens results in "on-target, off-tumor" toxicity. To unlock the full potential of AML therapies, we used CRISPR-Cas9 to genetically ablate the myeloid protein CD33 from healthy donor hematopoietic stem and progenitor cells (HSPCs), creating tremtelectogene empogeditemcel (trem-cel). Trem-cel is a HSPC transplant product designed to provide a reconstituted hematopoietic compartment that is resistant to anti-CD33 drug cytotoxicity. Here, we describe preclinical studies and process development of clinical-scale manufacturing of trem-cel. Preclinical data showed proof-of-concept with loss of CD33 surface protein and no impact on myeloid cell differentiation or function. At clinical scale, trem-cel could be manufactured reproducibly, routinely achieving >70% CD33 editing with no effect on cell viability, differentiation, and function. Trem-cel pharmacology studies using mouse xenograft models showed long-term engraftment, multilineage differentiation, and persistence of gene editing. Toxicology assessment revealed no adverse findings, and no significant or reproducible off-target editing events. Importantly, CD33-knockout myeloid cells were resistant to the CD33-targeted agent gemtuzumab ozogamicin in vitro and in vivo. These studies supported the initiation of the first-in-human, multicenter clinical trial evaluating the safety and efficacy of trem-cel in patients with AML (NCT04849910).

Single-cell atlas of healthy human blood unveils age-related loss of NKG2C+GZMB-CD8+ memory T cells and accumulation of type 2 memory T cells

Extensive, large-scale single-cell profiling of healthy human blood at different ages is one of the critical pending tasks required to establish a framework for the systematic understanding of human aging. Here, using single-cell RNA/T cell receptor (TCR)/BCR-seq with protein feature barcoding, we profiled 317 samples from 166 healthy individuals aged 25-85 years old. From this, we generated a dataset from ∼2 million cells that described 55 subpopulations of blood immune cells. Twelve subpopulations changed with age, including the accumulation of GZMK+CD8+ T cells and HLA-DR+CD4+ T cells. In contrast to other T cell memory subsets, transcriptionally distinct NKG2C+GZMB-CD8+ T cells counterintuitively decreased with age. Furthermore, we found a concerted age-associated increase in type 2/interleukin (IL)4-expressing memory subpopulations across CD4+ and CD8+ T cell compartments (CCR4+CD8+ Tcm and Th2 CD4+ Tmem), suggesting a systematic functional shift in immune homeostasis with age. Our work provides novel insights into healthy human aging and a comprehensive annotated resource.

A longitudinal single-cell atlas of treatment response in pediatric AML

Pediatric acute myeloid leukemia (pAML) is characterized by heterogeneous cellular composition, driver alterations and prognosis. Characterization of this heterogeneity and how it affects treatment response remains understudied in pediatric patients. We used single-cell RNA sequencing and single-cell ATAC sequencing to profile 28 patients representing different pAML subtypes at diagnosis, remission and relapse. At diagnosis, cellular composition differed between genetic subgroups. Upon relapse, cellular hierarchies transitioned toward a more primitive state regardless of subtype. Primitive cells in the relapsed tumor were distinct compared to cells at diagnosis, with under-representation of myeloid transcriptional programs and over-representation of other lineage programs. In some patients, this was accompanied by the appearance of a B-lymphoid-like hierarchy. Our data thus reveal the emergence of apparent subtype-specific plasticity upon treatment and inform on potentially targetable processes.

Age-Related Alterations in Peripheral Immune Landscape with Magnified Impact on Post-Stroke Brain

Immunosenescence refers to the multifaceted and profound alterations in the immune system brought about by aging, exerting complex influences on the pathophysiological processes of diseases that manifest upon it. Using a combination of single-cell RNA sequencing, cytometry by time of flight, and various immunological assays, we investigated the characteristics of immunosenescence in the peripheral blood of aged mice and its impact on the cerebral immune environment after ischemic stroke. Our results revealed some features of immunosenescence. We observed an increase in neutrophil counts, concurrent with accelerated neutrophil aging, characterized by altered expression of aging-associated markers like CD62L and consequential changes in neutrophil-mediated immune functions. Monocytes/macrophages in aged mice exhibited enhanced antigen-presentation capabilities. T cell profiles shifted from naive to effector or memory states, with a specific rise in T helper 1 cells and T helper 17 cells subpopulations and increased regulatory T cell activation in CD4 T cells. Furthermore, regulatory CD8 T cells marked by Klra decreased with aging, while a subpopulation of exhausted-like CD8 T cells expanded, retaining potent immunostimulatory and proinflammatory functions. Critically, these inherent disparities not only persisted but were further amplified within the ischemic hemispheres following stroke. In summary, our comprehensive insights into the key attributes of peripheral immunosenescence provide a vital theoretical foundation for understanding not only ischemic strokes but also other age-associated diseases.

Single-cell analysis of innate spinal cord regeneration identifies intersecting modes of neuronal repair

Adult zebrafish have an innate ability to recover from severe spinal cord injury. Here, we report a comprehensive single nuclear RNA sequencing atlas that spans 6 weeks of regeneration. We identify cooperative roles for adult neurogenesis and neuronal plasticity during spinal cord repair. Neurogenesis of glutamatergic and GABAergic neurons restores the excitatory/inhibitory balance after injury. In addition, transient populations of injury-responsive neurons (iNeurons) show elevated plasticity between 1 and 3 weeks post-injury. Using cross-species transcriptomics and CRISPR/Cas9 mutagenesis, we found iNeurons are injury-surviving neurons that share transcriptional similarities with a rare population of spontaneously plastic mouse neurons. iNeurons are required for functional recovery and employ vesicular trafficking as an essential mechanism that underlies neuronal plasticity. This study provides a comprehensive resource of the cells and mechanisms that direct spinal cord regeneration and establishes zebrafish as a model of plasticity-driven neural repair.

Tumor endothelial cell autophagy is a key vascular-immune checkpoint in melanoma

Tumor endothelial cells (TECs) actively repress inflammatory responses and maintain an immune-excluded tumor phenotype. However, the molecular mechanisms that sustain TEC-mediated immunosuppression remain largely elusive. Here, we show that autophagy ablation in TECs boosts antitumor immunity by supporting infiltration and effector function of T-cells, thereby restricting melanoma growth. In melanoma-bearing mice, loss of TEC autophagy leads to the transcriptional expression of an immunostimulatory/inflammatory TEC phenotype driven by heightened NF-kB and STING signaling. In line, single-cell transcriptomic datasets from melanoma patients disclose an enriched InflammatoryHigh /AutophagyLow TEC phenotype in correlation with clinical responses to immunotherapy, and responders exhibit an increased presence of inflamed vessels interfacing with infiltrating CD8+ T-cells. Mechanistically, STING-dependent immunity in TECs is not critical for the immunomodulatory effects of autophagy ablation, since NF-kB-driven inflammation remains functional in STING/ATG5 double knockout TECs. Hence, our study identifies autophagy as a principal tumor vascular anti-inflammatory mechanism dampening melanoma antitumor immunity.

Consensus scHPF Identifies Cell Type-Specific Drug Responses in Glioma by Integrating Large-Scale scRNA-seq

Single-cell transcriptomic analyses now frequently involve elaborate study designs including samples from multiple individuals, experimental conditions, perturbations, and batches from complex tissues. Dimensionality reduction is required to facilitate integration, interpretation, and statistical analysis. However, these datasets often include subtly different cellular subpopulations or state transitions, which are poorly described by clustering. We previously reported a Bayesian matrix factorization algorithm called single-cell hierarchical Poisson factorization (scHPF) that identifies gene co-expression patterns directly from single-cell RNA-seq (scRNA-seq) count matrices while accounting for transcript drop-out and noise. Here, we describe consensus scHPF, which analyzes scHPF models from multiple random initializations to identify the most robust gene signatures and automatically determine the number of factors for a given dataset. Consensus scHPF facilitates integration of complex datasets with highly multi-modal posterior distributions, resulting in factors that can be uniformly analyzed across individuals and conditions. To demonstrate the utility of consensus scHPF, we performed a meta-analysis of a large-scale scRNA-seq dataset from drug-treated, human glioma slice cultures generated from surgical specimens across three major cell types, 19 patients, 10 drug treatment conditions, and 52 samples. In addition to recapitulating previously reported cell type-specific drug responses from smaller studies, consensus scHPF identified disparate effects of the topoisomerase poisons etoposide and topotecan that are highly consistent with the distinct roles and expression patterns of their respective protein targets.

Immune checkpoints are predominantly co-expressed by clonally expanded CD4+FoxP3+ intratumoral T-cells in primary human cancers

BACKGROUND

In addition to anti-PD(L)1, anti-CTLA-4 and anti-LAG-3, novel immune checkpoint proteins (ICP)-targeted antibodies have recently failed to demonstrate significant efficacy in clinical trials. In these trials, patients were enrolled without screening for drug target expression. Although these novel ICP-targeted antibodies were expected to stimulate anti-tumor CD8 + T-cells, the rationale for their target expression in human tumors relied on pre-clinical IHC stainings and transcriptomic data, which are poorly sensitive and specific techniques for assessing membrane protein expression on immune cell subsets. Our aim was to describe ICP expression on intratumoral T-cells from primary solid tumors to better design upcoming neoadjuvant cancer immunotherapy trials.

METHODS

We prospectively performed multiparameter flow cytometry and single-cell RNA sequencing (scRNA-Seq) paired with TCR sequencing on freshly resected human primary tumors of various histological types to precisely determine ICP expression levels within T-cell subsets.

RESULTS

Within a given tumor type, we found high inter-individual variability for tumor infiltrating CD45 + cells and for T-cells subsets. The proportions of CD8+ T-cells (~ 40%), CD4+ FoxP3- T-cells (~ 40%) and CD4+ FoxP3+ T-cells (~ 10%) were consistent across patients and indications. Intriguingly, both stimulatory (CD25, CD28, 4-1BB, ICOS, OX40) and inhibitory (PD-1, CTLA-4, PD-L1, CD39 and TIGIT) checkpoint proteins were predominantly co-expressed by intratumoral CD4+FoxP3+ T-cells. ScRNA-Seq paired with TCR sequencing revealed that T-cells with high clonality and high ICP expressions comprised over 80% of FoxP3+ cells among CD4+ T-cells. Unsupervised clustering of flow cytometry and scRNAseq data identified subsets of CD8+ T-cells and of CD4+ FoxP3- T-cells expressing certain checkpoints, though these expressions were generally lower than in CD4+ FoxP3+ T-cell subsets, both in terms of proportions among total T-cells and ICP expression levels.

CONCLUSIONS

Tumor histology alone does not reveal the complete picture of the tumor immune contexture. In clinical trials, assumptions regarding target expression should rely on more sensitive and specific techniques than conventional IHC or transcriptomics. Flow cytometry and scRNAseq accurately characterize ICP expression within immune cell subsets. Much like in hematology, flow cytometry can better describe the immune contexture of solid tumors, offering the opportunity to guide patient treatment according to drug target expression rather than tumor histological type.

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.

Complementary and countervailing actions of Jak2 and Ikk2 in hematopoiesis in mice

Hyperactivation of JAK2 kinase is a unifying feature of human Ph- myeloproliferative neoplasms (MPNs), most commonly due to the JAK2 V617F mutation. Mice harboring a homologous mutation in the Jak2 locus exhibit a phenotype resembling polycythemia vera. NFκB pathway hyperactivation is present in myeloid neoplasms, including MPNs, despite scarcity of mutations in NFκB pathway genes. To determine the impact of NFκB pathway hyperactivation in conjunction with Jak2 V617F, we utilized Ikk2 (Ikk2-CA) mice. Pan-hematopoietic Ikk2-CA alone produced depletion of hematopoietic stem cells and B cells. When combined with the Jak2 V617F mutation, Ikk2-CA rescued the polycythemia vera phenotype of Jak2 V617F. Likewise, Jak2 V617F ameliorated defects in hematopoiesis produced by Ikk2-CA. Single-cell RNA sequencing of hematopoietic stem and progenitor cells revealed multiple genes antagonistically regulated by Jak2 and Ikk2, including subsets whose expression was altered by Jak2 V617F and/or Ikk2-CA but partly or fully rectified in the double mutant. We hypothesize that Jak2 promotes hematopoietic stem cell population self-renewal, whereas Ikk2 promotes myeloid lineage differentiation, and biases cell fates at several branch points in hematopoiesis. Jak2 and Ikk2 both regulate multiple genes affecting myeloid maturation and cell death. Therefore, the presence of dual Jak2 and NFκB hyperactivation may present neomorphic therapeutic vulnerabilities in myeloid neoplasms.

Rarity: discovering rare cell populations from single-cell imaging data

MOTIVATION

Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that is magnified when they are defined by differentially expressing a small number of genes.

RESULTS

Typical unsupervised approaches fail to identify such rare subpopulations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent, and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC datasets.

AVAILABILITY AND IMPLEMENTATION

Implementation of Rarity together with examples is available from the Github repository (https://github.com/kasparmartens/rarity).

Early β-amyloid accumulation in the brain is associated with peripheral T cell alterations

INTRODUCTION

Fast and minimally invasive approaches for early diagnosis of Alzheimer's disease (AD) are highly anticipated. Evidence of adaptive immune cells responding to cerebral β-amyloidosis has raised the question of whether immune markers could be used as proxies for β-amyloid accumulation in the brain.

METHODS

Here, we apply multidimensional mass-cytometry combined with unbiased machine-learning techniques to immunophenotype peripheral blood mononuclear cells from a total of 251 participants in cross-sectional and longitudinal studies.

RESULTS

We show that increases in antigen-experienced adaptive immune cells in the blood, particularly CD45RA-reactivated T effector memory (TEMRA) cells, are associated with early accumulation of brain β-amyloid and with changes in plasma AD biomarkers in still cognitively healthy subjects.

DISCUSSION

Our results suggest that preclinical AD pathology is linked to systemic alterations of the adaptive immune system. These immunophenotype changes may help identify and develop novel diagnostic tools for early AD assessment and better understand clinical outcomes.

Plasma protein signatures reflect systemic immunity and allograft function in kidney transplantation

Kidney transplantation causes large perturbations of the immune system. While many studies focus on the allograft, insights into systemic effects are largely missing. Here, we analyzed the systemic immune response in 3 cohorts of kidney transplanted patients. Using serum proteomics, laboratory values, mass cytometry, histological and clinical parameters, inter-patient heterogeneity was leveraged for multi-omic co-variation analysis. We identified circulating immune modules (CIM) that describe extra-renal signatures of co-regulated plasma proteins. CIM are present in nontransplanted controls, in transplant conditions and during rejection. They are enriched in pathways linked to kidney function, extracellular matrix, signaling, and cellular activation. A complex leukocyte response in the blood during allograft quiescence and rejection is associated with CIM activity and CIM-specific cytokines. CIM activity correlates with kidney function including a 2-month prediction. Together, the data suggest a systemic and multi-layered response of transplant immunity that might be insightful for understanding allograft dysfunction and developing translational biomarkers.

Multimodal spatiotemporal phenotyping of human retinal organoid development

Organoids generated from human pluripotent stem cells provide experimental systems to study development and disease, but quantitative measurements across different spatial scales and molecular modalities are lacking. In this study, we generated multiplexed protein maps over a retinal organoid time course and primary adult human retinal tissue. We developed a toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components and global patterning in each organoid and primary tissue. In addition, we generated a single-cell transcriptome and chromatin accessibility timecourse dataset and inferred a gene regulatory network underlying organoid development. We integrated genomic data with spatially segmented nuclei into a multimodal atlas to explore organoid patterning and retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death and showing that mosaic genetic perturbations in retinal organoids provide insight into cell fate regulation.

Omentum-derived matrix enables the study of metastatic ovarian cancer and stromal cell functions in a physiologically relevant environment

High-grade serous (HGS) ovarian cancer is the most lethal gynaecological disease in the world and metastases is a major cause. The omentum is the preferential metastatic site in HGS ovarian cancer patients and in vitro models that recapitulate the original environment of this organ at cellular and molecular level are being developed to study basic mechanisms that underpin this disease. The tumour extracellular matrix (ECM) plays active roles in HGS ovarian cancer pathology and response to therapy. However, most of the current in vitro models use matrices of animal origin and that do not recapitulate the complexity of the tumour ECM in patients. Here, we have developed omentum gel (OmGel), a matrix made from tumour-associated omental tissue of HGS ovarian cancer patients that has unprecedented similarity to the ECM of HGS omental tumours and is simple to prepare. When used in 2D and 3D in vitro assays to assess cancer cell functions relevant to metastatic ovarian cancer, OmGel performs as well as or better than the widely use Matrigel and does not induce additional phenotypic changes to ovarian cancer cells. Surprisingly, OmGel promotes pronounced morphological changes in cancer associated fibroblasts (CAFs). These changes were associated with the upregulation of proteins that define subsets of CAFs in tumour patient samples, highlighting the importance of using clinically and physiologically relevant matrices for in vitro studies. Hence, OmGel provides a step forward to study the biology of HGS omental metastasis. Metastasis in the omentum are also typical of other cancer types, particularly gastric cancer, implying the relevance of OmGel to study the biology of other highly lethal cancers.

Detection of continuous hierarchical heterogeneity by single-cell surface antigen analysis in the prognosis evaluation of acute myeloid leukaemia

BACKGROUND

Acute myeloid leukaemia (AML) is characterised by the malignant accumulation of myeloid progenitors with a high recurrence rate after chemotherapy. Blasts (leukaemia cells) exhibit a complete myeloid differentiation hierarchy hiding a wide range of temporal information from initial to mature clones, including genesis, phenotypic transformation, and cell fate decisions, which might contribute to relapse in AML patients.

METHODS

Based on the landscape of AML surface antigens generated by mass cytometry (CyTOF), we combined manifold analysis and principal curve-based trajectory inference algorithm to align myelocytes on a single-linear evolution axis by considering their phenotype continuum that correlated with differentiation order. Backtracking the trajectory from mature clusters located automatically at the terminal, we recurred the molecular dynamics during AML progression and confirmed the evolution stage of single cells. We also designed a 'dispersive antigens in neighbouring clusters exhibition (DANCE)' feature selection method to simplify and unify trajectories, which enabled the exploration and comparison of relapse-related traits among 43 paediatric AML bone marrow specimens.

RESULTS

The feasibility of the proposed trajectory analysis method was verified with public datasets. After aligning single cells on the pseudotime axis, primitive clones were recognized precisely from AML blasts, and the expression of the inner molecules before and after drug stimulation was accurately plotted on the trajectory. Applying DANCE to 43 clinical samples with different responses for chemotherapy, we selected 12 antigens as a general panel for myeloblast differentiation performance, and obtain trajectories to those patients. For the trajectories with unified molecular dynamics, CD11c overexpression in the primitive stage indicated a good chemotherapy outcome. Moreover, a later initial peak of stemness heterogeneity tended to be associated with a higher risk of relapse compared with complete remission.

CONCLUSIONS

In this study, pseudotime was generated as a new single-cell feature. Minute differences in temporal traits among samples could be exhibited on a trajectory, thus providing a new strategy for predicting AML relapse and monitoring drug responses over time scale.

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

CyGate Provides a Robust Solution for Automatic Gating of Single Cell Cytometry Data

To gain a better understanding of the complex human immune system, it is necessary to measure and interpret numerous cellular protein expressions at the single cell level. Mass cytometry is a relatively new technology that offers unprecedented information about the protein expression of a single cell. Conversely, the analysis of high-dimensional and multiparametric mass cytometric data sets presents a new computational challenge. For instance, conventional "manual gating" analysis was inefficient and unreliable for multiparametric phenotyping of the heterogeneous immune cellular system; consequently, automated methods have been developed to address the high dimensionality of mass cytometry data and enhance the reproducibility of the analysis. Here, we present CyGate, a semiautomated method for classifying single cells into their respective cell types. CyGate learns a gating strategy from a reference data set, trains a model for cell classification, and then automatically analyzes additional data sets using the trained model. CyGate also supports the machine learning framework for the classification of "ungated" cells, which are typically disregarded by automated methods. CyGate's utility was demonstrated by its high performance in cell type classification and the lowest generalization error on various public data sets when compared to the state-of-the-art semiautomated methods. Notably, CyGate had the shortest execution time, allowing it to scale with a growing number of samples. CyGate is available at https://github.com/seungjinna/cygate.

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.

Identification of drug candidates targeting monocyte reprogramming in people living with HIV

Introduction

People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers.

Methods

Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study.

Results

Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV.

Discussion

These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

Loss of p53-DREAM-mediated repression of cell cycle genes as a driver of lymph node metastasis in head and neck cancer

BACKGROUND

The prognosis for patients with head and neck cancer (HNC) is poor and has improved little in recent decades, partially due to lack of therapeutic options. To identify effective therapeutic targets, we sought to identify molecular pathways that drive metastasis and HNC progression, through large-scale systematic analyses of transcriptomic data.

METHODS

We performed meta-analysis across 29 gene expression studies including 2074 primary HNC biopsies to identify genes and transcriptional pathways associated with survival and lymph node metastasis (LNM). To understand the biological roles of these genes in HNC, we identified their associated cancer pathways, as well as the cell types that express them within HNC tumor microenvironments, by integrating single-cell RNA-seq and bulk RNA-seq from sorted cell populations.

RESULTS

Patient survival-associated genes were heterogenous and included drivers of diverse tumor biological processes: these included tumor-intrinsic processes such as epithelial dedifferentiation and epithelial to mesenchymal transition, as well as tumor microenvironmental factors such as T cell-mediated immunity and cancer-associated fibroblast activity. Unexpectedly, LNM-associated genes were almost universally associated with epithelial dedifferentiation within malignant cells. Genes negatively associated with LNM consisted of regulators of squamous epithelial differentiation that are expressed within well-differentiated malignant cells, while those positively associated with LNM represented cell cycle regulators that are normally repressed by the p53-DREAM pathway. These pro-LNM genes are overexpressed in proliferating malignant cells of TP53 mutated and HPV + ve HNCs and are strongly associated with stemness, suggesting that they represent markers of pre-metastatic cancer stem-like cells. LNM-associated genes are deregulated in high-grade oral precancerous lesions, and deregulated further in primary HNCs with advancing tumor grade and deregulated further still in lymph node metastases.

CONCLUSIONS

In HNC, patient survival is affected by multiple biological processes and is strongly influenced by the tumor immune and stromal microenvironments. In contrast, LNM appears to be driven primarily by malignant cell plasticity, characterized by epithelial dedifferentiation coupled with EMT-independent proliferation and stemness. Our findings postulate that LNM is initially caused by loss of p53-DREAM-mediated repression of cell cycle genes during early tumorigenesis.

Cancer immunotherapy via synergistic coactivation of myeloid receptors CD40 and Dectin-1

Myeloid cells facilitate T cell immune evasion in cancer yet are pliable and have antitumor potential. Here, by cotargeting myeloid activation molecules, we leveraged the myeloid compartment as a therapeutic vulnerability in mouse models of pancreatic cancer. Myeloid cells in solid tumors expressed activation receptors including the pattern recognition receptor Dectin-1 and the TNF receptor superfamily member CD40. In mouse models of checkpoint inhibitor-resistant pancreatic cancer, coactivation of Dectin-1, via systemic β-glucan therapy, and CD40, with agonist antibody treatment, eradicated established tumors and induced immunological memory. Antitumor activity was dependent on cDC1s and T cells but did not require classical T cell-mediated cytotoxicity or blockade of checkpoint molecules. Rather, targeting CD40 drove T cell-mediated IFN-γ signaling, which converged with Dectin-1 activation to program distinct macrophage subsets to facilitate tumor responses. Thus, productive cancer immune surveillance in pancreatic tumors resistant to checkpoint inhibition can be invoked by coactivation of complementary myeloid signaling pathways.

High-dimensional mass cytometry reveals systemic and local immune signatures in necrotizing enterocolitis

Objective

Patients with necrotizing enterocolitis display severe gastrointestinal complications of prematurity, but the mechanism driving this clinical profile remains unknown. We used mass cytometry time-of-flight to characterize and compare immune cell populations in the blood and intestine tissue from patients with and without (controls) necrotizing enterocolitis at single-cell resolution.

Methods

We completed a deep mapping of the immune system of the peripheral blood mononuclear cells and intestinal mucosa tissue using mass cytometry to evaluate immune cell types, which revealed global immune dysregulation characteristics underlying necrotizing enterocolitis.

Results

Compared with controls, natural killer cells display signs of heightened activation and increased cytotoxic potential in the peripheral blood and mucosa of patients with necrotizing enterocolitis. Furthermore, CD4+ T effector memory cells, non-classical monocytes, active dendritic cells, and neutrophils were specifically enriched in the mucosa, suggesting trafficking from the periphery to areas of inflammation. Moreover, we mapped the systemic and local distinct immune signatures suggesting patterns of cell localization in necrotizing enterocolitis.

Conclusion

We used mass cytometry time-of-flight technology to identify immune cell populations specific to the peripheral blood and intestinal mucosa tissue from patients with necrotizing enterocolitis and controls. This information might be used to develop precise diagnosis and therapies that target specific cell populations in patients with necrotizing enterocolitis.

DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data

Characterizing cell-cell communication and tracking its variability over time is essential for understanding the coordination of biological processes mediating normal development, progression of disease, or responses to perturbations such as therapies. Existing tools lack the ability to capture time-dependent intercellular interactions, such as those influenced by therapy, and primarily rely on existing databases compiled from limited contexts. We present DIISCO, a Bayesian framework for characterizing the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method uses structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their co-evolution and incorporates prior knowledge of receptor-ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from CAR-T cells co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk.

Cytomulate: accurate and efficient simulation of CyTOF data

Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter, and generative models like LAMBDA.

Mesotrode chronic simultaneous mesoscale cortical imaging and subcortical or peripheral nerve spiking activity recording in mice

Brain function originates from hierarchical spatial-temporal neural dynamics distributed across cortical and subcortical networks. However, techniques available to assess large-scale brain network activity with single-neuron resolution in behaving animals remain limited. Here, we present Mesotrode that integrates chronic wide-field mesoscale cortical imaging and compact multi-site cortical/subcortical cellular electrophysiology in head-fixed mice that undergo self-initiated running or orofacial movements. Specifically, we harnessed the flexibility of chronic multi-site tetrode recordings to monitor single-neuron activity in multiple subcortical structures while simultaneously imaging the mesoscale activity of the entire dorsal cortex. A mesoscale spike-triggered averaging procedure allowed the identification of cortical activity motifs preferentially associated with single-neuron spiking. Using this approach, we were able to characterize chronic single-neuron-related functional connectivity maps for up to 60 days post-implantation. Neurons recorded from distinct subcortical structures display diverse but segregated cortical maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. We extended the capability of Mesotrode by implanting the micro-electrode at the facial motor nerve and found that facial nerve spiking is functionally associated with the PTA, RSP, and M2 network, and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice. These findings demonstrate that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods, generating multimodal and multi-scale network activity from a single implant, offering new insights into the neural mechanisms underlying specific behaviors.

Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Advances in chimeric antigen receptor (CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies. However, progress is still hindered as clinical benefit is only available for a fraction of patients. A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice. Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design, guide gene-based T cell modification, and optimize the CAR-T manufacturing conditions, and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes. The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities. In this review, we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies. We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy. Specifically, we provide an overview of single-cell studies focusing on target antigens, CAR-transgene integration, and preclinical research and clinical applications, and then discuss how it will affect the future of CAR-T cell therapy.

Single cell spatial analysis reveals inflammatory foci of immature neutrophil and CD8 T cells in COVID-19 lungs

Single cell spatial interrogation of the immune-structural interactions in COVID -19 lungs is challenging, mainly because of the marked cellular infiltrate and architecturally distorted microstructure. To address this, we develop a suite of mathematical tools to search for statistically significant co-locations amongst immune and structural cells identified using 37-plex imaging mass cytometry. This unbiased method reveals a cellular map interleaved with an inflammatory network of immature neutrophils, cytotoxic CD8 T cells, megakaryocytes and monocytes co-located with regenerating alveolar progenitors and endothelium. Of note, a highly active cluster of immature neutrophils and CD8 T cells, is found spatially linked with alveolar progenitor cells, and temporally with the diffuse alveolar damage stage. These findings offer further insights into how immune cells interact in the lungs of severe COVID-19 disease. We provide our pipeline [Spatial Omics Oxford Pipeline (SpOOx)] and visual-analytical tool, Multi-Dimensional Viewer (MDV) software, as a resource for spatial analysis.

Defective LAT signalosome pathology in mice mimics human IgG4-related disease at single-cell level

Mice with a loss-of-function mutation in the LAT adaptor (LatY136F) develop an autoimmune and type 2 inflammatory disorder called defective LAT signalosome pathology (DLSP). We analyzed via single-cell omics the trajectory leading to LatY136F DLSP and the underlying CD4+ T cell diversification. T follicular helper cells, CD4+ cytotoxic T cells, activated B cells, and plasma cells were found in LatY136F spleen and lung. Such cell constellation entailed all the cell types causative of human IgG4-related disease (IgG4-RD), an autoimmune and inflammatory condition with LatY136F DLSP-like histopathological manifestations. Most previously described T cell-mediated autoimmune manifestations require persistent TCR input. In contrast, following their first engagement by self-antigens, the autoreactive TCR expressed by LatY136F CD4+ T cells hand over their central role in T cell activation to CD28 costimulatory molecules. As a result, all subsequent LatY136F DLSP manifestations, including the production of autoantibodies, solely rely on CD28 engagement. Our findings elucidate the etiology of the LatY136F DLSP and qualify it as a model of IgG4-RD.

KMD clustering: robust general-purpose clustering of biological data

The noisy and high-dimensional nature of biological data has spawned advanced clustering algorithms that are tailored for specific biological datatypes. However, the performance of such methods varies greatly between datasets and they require post hoc tuning of cryptic hyperparameters. We present k minimal distance (KMD) clustering, a general-purpose method based on a generalization of single and average linkage hierarchical clustering. We introduce a generalized silhouette-like function to eliminate the cryptic hyperparameter k, and use sampling to enable application to million-object datasets. Rigorous comparisons to general and specialized clustering methods on simulated, mass cytometry and scRNA-seq datasets show consistent high performance of KMD clustering across all datasets.

MetaGate: Interactive Analysis of High-Dimensional Cytometry Data with Meta Data Integration

Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level, widely used in basic research and routine clinical diagnostics. Traditionally, data analysis is carried out using manual gating, in which cut-offs are defined manually for each marker. Recent technical advances, including the introduction of mass cytometry, have increased the number of proteins that can be simultaneously assessed in each cell. To tackle the resulting escalation in data complexity, numerous new analysis algorithms have been developed. However, many of these show limitations in terms of providing statistical testing, data sharing, cross-experiment comparability integration with clinical data. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of clinical meta data. MetaGate allows manual gating to take place in traditional cytometry analysis software, while providing a combinatorial gating system for simple and transparent definition of biologically relevant cell populations. We demonstrate the utility of MetaGate through a comprehensive analysis of peripheral blood immune cells from 28 patients with diffuse large B-cell lymphoma (DLBCL) along with 17 age- and sex-matched healthy controls using two mass cytometry panels made of a total of 55 phenotypic markers. In a two-step process, raw data from 143 FCS files is first condensed through a data reduction algorithm and combined with information from manual gates, user-defined cellular populations and clinical meta data. This results in one single small project file containing all relevant information to allow rapid statistical calculation and visualization of any desired comparison, including box plots, heatmaps and volcano plots. Our detailed characterization of the peripheral blood immune cell repertoire in patients with DLBCL corroborate previous reports showing expansion of monocytic myeloid-derived suppressor cells, as well as an inverse correlation between NK cell numbers and disease progression.

An end-to-end workflow for multiplexed image processing and analysis

Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .

Unified Mouse and Human Kidney Single-Cell Expression Atlas Reveal Commonalities and Differences in Disease States

SIGNIFICANCE STATEMENT

Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. The authors characterized 18 different mouse kidney disease models at both bulk and single-cell gene expression levels and compared single-cell gene expression data from diabetic kidney disease (DKD) mice and from patients with DKD. Although single cell-level gene expression changes were mostly model-specific, different disease models showed similar changes when compared at a pathway level. The authors also found that changes in fractions of cell types are major drivers of bulk gene expression differences. Although the authors found only a small overlap of single cell-level gene expression changes between the mouse DKD model and patients, they observed consistent pathway-level changes.

BACKGROUND

Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared.

METHODS

We analyzed single-cell RNA sequencing data (36 samples) and bulk gene expression data (42 samples) from 18 commonly used mouse kidney disease models. We compared single-nucleus RNA sequencing data from a mouse diabetic kidney disease model with data from patients with diabetic kidney disease and healthy controls.

RESULTS

We generated a uniformly processed mouse single-cell atlas containing information for nearly 300,000 cells, identifying all major kidney cell types and states. Our analysis revealed that changes in fractions of cell types are major drivers of differences in bulk gene expression. Although gene expression changes at the single-cell level were mostly model-specific, different disease models showed similar changes when compared at a pathway level. Tensor decomposition analysis highlighted the important changes in proximal tubule cells in disease states. Specifically, we identified important alterations in expression of metabolic and inflammation-associated pathways. The mouse diabetic kidney disease model and patients with diabetic kidney disease shared only a small number of conserved cell type-specific differentially expressed genes, but we observed pathway-level activation patterns conserved between mouse and human diabetic kidney disease samples.

CONCLUSIONS

This study provides a comprehensive mouse kidney single-cell atlas and defines gene expression commonalities and differences in disease states in mice. The results highlight the key role of cell heterogeneity in driving changes in bulk gene expression and the limited overlap of single-cell gene expression changes between animal models and patients, but they also reveal consistent pathway-level changes.