Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states

Efficient Strategy to Discover DNA Aptamers Against Low Abundance Cell Surface Proteins in Scarce Samples

Molecular recognition probes targeting cell surface proteins such as aptamers play crucial roles in precise diagnostics and therapy. However, the selection of aptamers against low-abundance proteins in situ on the cell surface, especially in scarce samples, remains an unmet challenge. In this study, we present a single-round, single-cell aptamer selection method by employing a digital DNA sequencing strategy, termed DiDS selection, to address this dilemma. This approach incorporates a molecular identification card for each DNA template, thereby mitigating biases introduced by multiple PCR amplifications and ensuring the accurate identification of aptamer candidates. Through DiDS selection, we successfully obtained a series of high-quality aptamers against cell lines, clinical specimens, and neurons. Subsequent analyses for target identification revealed that aptamers derived from DiDS selection exhibit recognition capabilities for proteins with varying abundance levels. In contrast, multiple rounds of selection resulted in the enrichment of only one aptamer targeting a high-abundance target. Moreover, the comprehensive profiling of cell surfaces at the single-cell level, utilizing an enriched aptamer pool, revealed unique molecular patterns for each cell line. This streamlined approach holds promise for the rapid generation of specific recognition molecules targeting cell surface proteins across a broad range of expression levels and expands its applications in cell profiling, specific probe identification, biomarker discovery, etc.

Imputing abundance of over 2,500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles

Cell surface proteins serve as primary drug targets and cell identity markers. Techniques such as CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) have enabled the simultaneous quantification of surface protein abundance and transcript expression within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability of these computational approaches across diverse contexts (e.g., different tissues/disease states) impede their widespread adoption. Here, we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA sequencing), a context-agnostic zero-shot deep ensemble model, which enables large-scale protein abundance prediction and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2,500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER, including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer. A record of this paper's transparent peer review process is included in the supplemental information.

Network-Constrained Eigen-Single-Cell Profile Estimation for Uncovering Crucial Immunogene Regulatory Systems in Human Bone Marrow

We focus on characterizing cell lines from young and aged-healthy and -AML (acute myeloid leukemia) cell lines, and our goal is to identify the key markers associated with the progression of AML. To characterize the age-related phenotypes in AML cell lines, we consider eigenCell analysis that effectively encapsulates the primary expression level patterns across the cell lines. However, earlier investigations utilizing eigenGenes and eigenCells analysis were based on linear combination of all features, leading to the disturbance from noise features. Moreover, the analysis based on a fully dense loading matrix makes it challenging to interpret the results of eigenCells analysis. In order to address these challenges, we develop a novel computational approach termed network-constrained eigenCells profile estimation, which employs a sparse learning strategy. The proposed method estimates eigenCell based on not only the lasso but also network constrained penalization. The use of the network-constrained penalization enables us to simultaneously select neighborhood genes. Furthermore, the hub genes and their regulator/target genes are easily selected as crucial markers for eigenCells estimation. That is, our method can incorporate insights from network biology into the process of sparse loading estimation. Through our methodology, we estimate sparse eigenCells profiles, where only critical markers exhibit expression levels. This allows us to identify the key markers associated with a specific phenotype. Monte Carlo simulations demonstrate the efficacy of our method in reconstructing the sparse structure of eigenCells profiles. We employed our approach to unveil the regulatory system of immunogenes in both young/aged-healthy and -AML cell lines. The markers we have identified for the age-related phenotype in both healthy and AML cell lines have garnered strong support from previous studies. Specifically, our findings, in conjunction with the existing literature, indicate that the activities within this subnetwork of CD79A could be pivotal in elucidating the mechanism driving AML progression, particularly noting the significant role played by the diminished activities in the CD79A subnetwork. We expect that the proposed method will be a useful tool for characterizing disease-related subsets of cell lines, encompassing phenotypes and clones.

New frameworks for hematopoiesis derived from single-cell genomics

ABSTRACT

Recent advancements in single-cell genomics have enriched our understanding of hematopoiesis, providing intricate details about hematopoietic stem cell biology, differentiation, and lineage commitment. Technological advancements have highlighted extensive heterogeneity of cell populations and continuity of differentiation routes. Nevertheless, intermediate "attractor" states signify structure in stem and progenitor populations that link state transition dynamics to fate potential. We discuss how innovative model systems quantify lineage bias and how stress accelerates differentiation, thereby reducing fate plasticity compared with native hematopoiesis. We conclude by offering our perspective on the current model of hematopoiesis and discuss how a more precise understanding can translate to strategies that extend healthy hematopoiesis and prevent disease.

Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles

Cell surface proteins serve as primary drug targets and cell identity markers. The emergence of techniques like CITE-seq has enabled simultaneous quantification of surface protein abundance and transcript expression for multimodal data analysis within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance based solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability for these computational approaches across diverse contexts, such as different tissues or disease states, impede their widespread adoption. Here we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA-seq), a context-agnostic zero-shot deep ensemble model, which enables the large-scale prediction of cell surface protein abundance and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer.

Compound-SNE: Comparative alignment of t-SNEs for multiple single-cell omics data visualisation

SUMMARY

One of the first steps in single-cell omics data analysis is visualization, which allows researchers to see how well-separated cell-types are from each other. When visualizing multiple datasets at once, data integration/batch correction methods are used to merge the datasets. While needed for downstream analyses, these methods modify features space (e.g. gene expression)/PCA space in order to mix cell-types between batches as well as possible. This obscures sample-specific features and breaks down local embedding structures that can be seen when a sample is embedded alone. Therefore, in order to improve in visual comparisons between large numbers of samples (e.g., multiple patients, omic modalities, different time points), we introduce Compound-SNE, which performs what we term a soft alignment of samples in embedding space. We show that Compound-SNE is able to align cell-types in embedding space across samples, while preserving local embedding structures from when samples are embedded independently.

AVAILABILITY AND IMPLEMENTATION

Python code for Compound-SNE is available for download at https://github.com/HaghverdiLab/Compound-SNE.

SUPPLEMENTARY INFORMATION

Available online. Provides algorithmic details and additional tests.

Automated cell type annotation and exploration of single-cell signaling dynamics using mass cytometry

Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterization of cellular heterogeneity in cancer and other diseases. Unfortunately, high-dimensional analyses of CyTOF data remain quite demanding. Here, we deploy a bioinformatics framework that tackles two fundamental problems in CyTOF analyses namely (1) automated annotation of cell populations guided by a reference dataset and (2) systematic utilization of single-cell data for effective patient stratification. By applying this framework on several publicly available datasets, we demonstrate that the Scaffold approach achieves good trade-off between sensitivity and specificity for automated cell type annotation. Additionally, a case study focusing on a cohort of 43 leukemia patients reported salient interactions between signaling proteins that are sufficient to predict short-term survival at time of diagnosis using the XGBoost algorithm. Our work introduces an automated and versatile analysis framework for CyTOF data with many applications in future precision medicine projects.

Unveiling mitochondrial and ribosomal gene deregulation and tumor microenvironment dynamics in acute myeloid leukemia

Acute myeloid leukemia (AML) is a malignant clonal hematopoietic disease with a poor prognosis. Understanding the interaction between leukemic cells and the tumor microenvironment (TME) can help predict the prognosis of leukemia and guide its treatment. Re-analyzing the scRNA-seq data from the CSC and G20 cohorts, using a Python-based pipeline including machine-learning-based scVI-tools, recapitulated the distinct hierarchical structure within the samples of AML patients. Weighted correlation network analysis (WGCNA) was conducted to construct a weighted gene co-expression network and to identify gene modules primarily focusing on hematopoietic stem cells (HSCs), multipotent progenitors (MPPs), and natural killer (NK) cells. The analysis revealed significant deregulation in gene modules associated with aerobic respiration and ribosomal/cytoplasmic translation. Cell-cell communications were elucidated by the CellChat package, revealing an imbalance of activating and inhibitory immune signaling pathways. Interception of genes upregulated in leukemic HSCs & MPPs as well as in NKG2A-high NK cells was used to construct prognostic models. Normal Cox and artificial neural network models based on 10 genes were developed. The study reveals the deregulation of mitochondrial and ribosomal genes in AML patients and suggests the co-occurrence of stimulatory and inhibitory factors in the AML TME.

Integration of spatial and single-cell data across modalities with weakly linked features

Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

A new model of human lymphopoiesis across development and aging

Over the past decade our research has implemented a multimodal approach to human lymphopoiesis, combining clonal-scale mapping of lymphoid developmental architecture with the monitoring of dynamic changes in the pattern of lymphocyte generation across ontogeny. We propose that lymphopoiesis stems from founder populations of CD127/interleukin (IL)7R- or CD127/IL7R+ early lymphoid progenitors (ELPs) polarized respectively toward the T-natural killer (NK)/innate lymphoid cell (ILC) or B lineages, arising from newly characterized CD117lo multi-lymphoid progenitors (MLPs). Recent data on the lifelong lymphocyte dynamics of healthy donors suggest that, after birth, lymphopoiesis may become increasingly oriented toward the production of B lymphocytes. Stemming from this, we posit that there are three major developmental transitions, the first occurring during the neonatal period, the next at puberty, and the last during aging.

High percentage of bone marrow CD8+ tissue-resident-like memory T cells predicts inferior survival in patients with acute myeloid leukemia

Tissue-resident memory T (TRM) cells infiltrating solid tumors could influence tumor progression and the response to immune therapies. However, the proportion and prognostic value of TRM cells in the bone marrow (BM) of patients with acute myeloid leukemia (AML) are unclear. In this study, we used flow cytometry to assay the phenotype of 49 BM samples from patients newly diagnosed with AML (ND-AML). We found that the BM CD8+ effector memory (TEM) cells highly expressed CD69 (CD8+ TRM-like T cells), and their percentage was significantly increased in patients with ND-AML compared with that in healthy individuals (HI). The high percentage of CD8+ TRM-like subset was associated with poor overall survival in our ND-AML cohort. The Kaplan-Meier Plotter database verified a significantly reduced survival rate among patients with high expression of CD8+ TRM-like T cell characteristic genes (CD8A, CD69, and TOX), especially the M4 and M5 subtypes. Phenotypic analysis revealed that the BM CD8+ TRM-like subpopulation exhibited exhausted T cell characteristics, but its high expression of CD27 and CD28 and low expression of CD57 suggested its high proliferative potential. The single-cell proteogenomic dataset confirmed the existence of TRM-like CD8+ T cells in the BM of patients with AML and verified the high expression of immune checkpoints and costimulatory molecules. In conclusion, we found that the accumulation of BM CD8+ TRM-like cells could be an immune-related survival prediction marker for patients with AML.

Single-cell genomics-based immune and disease monitoring in blood malignancies

Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Non-viral DNA delivery and TALEN editing correct the sickle cell mutation in hematopoietic stem cells

Sickle cell disease is a devastating blood disorder that originates from a single point mutation in the HBB gene coding for hemoglobin. Here, we develop a GMP-compatible TALEN-mediated gene editing process enabling efficient HBB correction via a DNA repair template while minimizing risks associated with HBB inactivation. Comparing viral versus non-viral DNA repair template delivery in hematopoietic stem and progenitor cells in vitro, both strategies achieve comparable HBB correction and result in over 50% expression of normal adult hemoglobin in red blood cells without inducing β-thalassemic phenotype. In an immunodeficient female mouse model, transplanted cells edited with the non-viral strategy exhibit higher engraftment and gene correction levels compared to those edited with the viral strategy. Transcriptomic analysis reveals that non-viral DNA repair template delivery mitigates P53-mediated toxicity and preserves high levels of long-term hematopoietic stem cells. This work paves the way for TALEN-based autologous gene therapy for sickle cell disease.

Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging

Non-hematopoietic cells are essential contributors to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed co-detection by indexing (CODEX) to spatially profile over 1.2 million cells. We integrated scRNA-seq and CODEX data to link predicted cellular signaling with spatial proximity. Our analysis revealed a hyperoxygenated arterio-endosteal neighborhood for early myelopoiesis, and an adipocytic localization for early hematopoietic stem and progenitor cells (HSPCs). We used our CODEX atlas to annotate new images and uncovered mesenchymal stromal cell (MSC) expansion and spatial neighborhoods co-enriched for leukemic blasts and MSCs in acute myeloid leukemia (AML) patient samples. This spatially resolved, multiomic atlas of human bone marrow provides a reference for investigation of cellular interactions that drive hematopoiesis.

A transcriptomic based deconvolution framework for assessing differentiation stages and drug responses of AML

The diagnostic spectrum for AML patients is increasingly based on genetic abnormalities due to their prognostic and predictive value. However, information on the AML blast phenotype regarding their maturational arrest has started to regain importance due to its predictive power for drug responses. Here, we deconvolute 1350 bulk RNA-seq samples from five independent AML cohorts on a single-cell healthy BM reference and demonstrate that the morphological differentiation stages (FAB) could be faithfully reconstituted using estimated cell compositions (ECCs). Moreover, we show that the ECCs reliably predict ex-vivo drug resistances as demonstrated for Venetoclax, a BCL-2 inhibitor, resistance specifically in AML with CD14+ monocyte phenotype. We validate these predictions using LUMC proteomics data by showing that BCL-2 protein abundance is split into two distinct clusters for NPM1-mutated AML at the extremes of CD14+ monocyte percentages, which could be crucial for the Venetoclax dosing patients. Our results suggest that Venetoclax resistance predictions can also be extended to AML without recurrent genetic abnormalities and possibly to MDS-related and secondary AML. Lastly, we show that CD14+ monocytic dominated Ven/Aza treated patients have significantly lower overall survival. Collectively, we propose a framework for allowing a joint mutation and maturation stage modeling that could be used as a blueprint for testing sensitivity for new agents across the various subtypes of AML.

A single cell framework identifies functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis

Hematopoietic multipotent progenitors (MPPs) regulate blood cell production to appropriately meet the biological demands of the human body. Human MPPs remain ill-defined whereas mouse MPPs have been well characterized with distinct immunophenotypes and lineage potencies. Using multiomic single cell analyses and complementary functional assays, we identified new human MPPs and oligopotent progenitor populations within Lin-CD34+CD38dim/lo adult bone marrow with distinct biomolecular and functional properties. These populations were prospectively isolated based on expression of CD69, CLL1, and CD2 in addition to classical markers like CD90 and CD45RA. We show that within the canonical Lin-CD34+CD38dim/loCD90CD45RA-MPP population, there is a CD69+ MPP with long-term engraftment and multilineage differentiation potential, a CLL1+ myeloid-biased MPP, and a CLL1-CD69-erythroid-biased MPP. We also show that the canonical Lin-CD34+CD38dim/loCD90-CD45RA+ LMPP population can be separated into a CD2+ LMPP with lymphoid and myeloid potential, a CD2-LMPP with high lymphoid potential, and a CLL1+ GMP with minimal lymphoid potential. We used these new HSPC profiles to study human and mouse bone marrow cells and observe limited cell type specific homology between humans and mice and cell type specific changes associated with aging. By identifying and functionally characterizing new adult MPP sub-populations, we provide an updated reference and framework for future studies in human hematopoiesis.

Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche

Clonal hematopoiesis (CH) defines a premalignant state predominantly found in older persons that increases the risk of developing hematologic malignancies and age-related inflammatory diseases. However, the risk for malignant transformation or non-malignant disorders is variable and difficult to predict, and defining the clinical relevance of specific candidate driver mutations in individual carriers has proved to be challenging. In addition to the cell-intrinsic mechanisms, mutant cells rely on and alter cell-extrinsic factors from the bone marrow (BM) niche, which complicates the prediction of a mutant cell's fate in a shifting pre-malignant microenvironment. Therefore, identifying the insidious and potentially broad impact of driver mutations on supportive niches and immune function in CH aims to understand the subtle differences that enable driver mutations to yield different clinical outcomes. Here, we review the changes in the aging BM niche and the emerging evidence supporting the concept that CH can progressively alter components of the local BM microenvironment. These alterations may have profound implications for the functionality of the osteo-hematopoietic niche and overall bone health, consequently fostering a conducive environment for the continued development and progression of CH. We also provide an overview of the latest technology developments to study the spatiotemporal dependencies in the CH BM niche, ideally in the context of longitudinal studies following CH over time. Finally, we discuss aspects of CH carrier management in clinical practice, based on work from our group and others.

Single-cell systems pharmacology identifies development-driven drug response and combination therapy in B cell acute lymphoblastic leukemia

Leukemia can arise at various stages of the hematopoietic differentiation hierarchy, but the impact of developmental arrest on drug sensitivity is unclear. Applying network-based analyses to single-cell transcriptomes of human B cells, we define genome-wide signaling circuitry for each B cell differentiation stage. Using this reference, we comprehensively map the developmental states of B cell acute lymphoblastic leukemia (B-ALL), revealing its strong correlation with sensitivity to asparaginase, a commonly used chemotherapeutic agent. Single-cell multi-omics analyses of primary B-ALL blasts reveal marked intra-leukemia heterogeneity in asparaginase response: resistance is linked to pre-pro-B-like cells, with sensitivity associated with the pro-B-like population. By targeting BCL2, a driver within the pre-pro-B-like cell signaling network, we find that venetoclax significantly potentiates asparaginase efficacy in vitro and in vivo. These findings demonstrate a single-cell systems pharmacology framework to predict effective combination therapies based on intra-leukemia heterogeneity in developmental state, with potentially broad applications beyond B-ALL.

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 ).

An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors

Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.

Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging

The bone marrow is the organ responsible for blood production. Diverse non-hematopoietic cells contribute essentially to hematopoiesis. However, these cells and their spatial organization remain largely uncharacterized as they have been technically challenging to study in humans. Here, we used fresh femoral head samples and performed single-cell RNA sequencing (scRNA-Seq) to profile 29,325 enriched non-hematopoietic bone marrow cells and discover nine transcriptionally distinct subtypes. We next employed CO-detection by inDEXing (CODEX) multiplexed imaging of 18 individuals, including both healthy and acute myeloid leukemia (AML) samples, to spatially profile over one million single cells with a novel 53-antibody panel. We discovered a relatively hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, niche for early hematopoietic stem and progenitor cells. We used our atlas to predict cell type labels in new bone marrow images and used these predictions to uncover mesenchymal stromal cell (MSC) expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. Our work represents the first comprehensive, spatially-resolved multiomic atlas of human bone marrow and will serve as a reference for future investigation of cellular interactions that drive hematopoiesis.

A 19-color single-tube full spectrum flow cytometry assay for the detection of measurable residual disease in acute myeloid leukemia

Multiparameter flow cytometry (MFC) has emerged as a standard method for quantifying measurable residual disease (MRD) in acute myeloid leukemia. However, the limited number of available channels on conventional flow cytometers requires the division of a diagnostic sample into several tubes, restricting the number of cells and the complexity of immunophenotypes that can be analyzed. Full spectrum flow cytometers overcome this limitation by enabling the simultaneous use of up to 40 fluorescent markers. Here, we used this approach to develop a good laboratory practice-conform single-tube 19-color MRD detection assay that complies with recommendations of the European LeukemiaNet Flow-MRD Working Party. We based our assay on clinically-validated antibody clones and evaluated its performance on an IVD-certified full spectrum flow cytometer. We measured MRD and normal bone marrow samples and compared the MRD data to a widely used reference MRD-MFC panel generating highly concordant results. Using our newly developed single-tube panel, we established reference values in healthy bone marrow for 28 consensus leukemia-associated immunophenotypes and introduced a semi-automated dimensionality-reduction, clustering and cell type identification approach that aids the unbiased detection of aberrant cells. In summary, we provide a comprehensive full spectrum MRD-MFC workflow with the potential for rapid implementation for routine diagnostics due to reduced cell requirements and ease of data analysis with increased reproducibility in comparison to conventional FlowMRD routines.

An atlas of cells in the human tonsil

Palatine tonsils are secondary lymphoid organs (SLOs) representing the first line of immunological defense against inhaled or ingested pathogens. We generated an atlas of the human tonsil composed of >556,000 cells profiled across five different data modalities, including single-cell transcriptome, epigenome, proteome, and immune repertoire sequencing, as well as spatial transcriptomics. This census identified 121 cell types and states, defined developmental trajectories, and enabled an understanding of the functional units of the tonsil. Exemplarily, we stratified myeloid slan-like subtypes, established a BCL6 enhancer as locally active in follicle-associated T and B cells, and identified SIX5 as putative transcriptional regulator of plasma cell maturation. Analyses of a validation cohort confirmed the presence, annotation, and markers of tonsillar cell types and provided evidence of age-related compositional shifts. We demonstrate the value of this resource by annotating cells from B cell-derived mantle cell lymphomas, linking transcriptional heterogeneity to normal B cell differentiation states of the human tonsil.

Collaborative effect of Csnk1a1 haploinsufficiency and mutant p53 in Myc induction can promote leukemic transformation

ABSTRACT

It is still not fully understood how genetic haploinsufficiency in del(5q) myelodysplastic syndrome (MDS) contributes to malignant transformation of hematopoietic stem cells. We asked how compound haploinsufficiency for Csnk1a1 and Egr1 in the common deleted region on chromosome 5 affects hematopoietic stem cells. Additionally, Trp53 was disrupted as the most frequently comutated gene in del(5q) MDS using CRISPR/Cas9 editing in hematopoietic progenitors of wild-type (WT), Csnk1a1-/+, Egr1-/+, Csnk1a1/Egr1-/+ mice. A transplantable acute leukemia only developed in the Csnk1a1-/+Trp53-edited recipient. Isolated blasts were indefinitely cultured ex vivo and gave rise to leukemia after transplantation, providing a tool to study disease mechanisms or perform drug screenings. In a small-scale drug screening, the collaborative effect of Csnk1a1 haploinsufficiency and Trp53 sensitized blasts to the CSNK1 inhibitor A51 relative to WT or Csnk1a1 haploinsufficient cells. In vivo, A51 treatment significantly reduced blast counts in Csnk1a1 haploinsufficient/Trp53 acute leukemias and restored hematopoiesis in the bone marrow. Transcriptomics on blasts and their normal counterparts showed that the derived leukemia was driven by MAPK and Myc upregulation downstream of Csnk1a1 haploinsufficiency cooperating with a downregulated p53 axis. A collaborative effect of Csnk1a1 haploinsufficiency and p53 loss on MAPK and Myc upregulation was confirmed on the protein level. Downregulation of Myc protein expression correlated with efficient elimination of blasts in A51 treatment. The "Myc signature" closely resembled the transcriptional profile of patients with del(5q) MDS with TP53 mutation.

Biological basis of extensive pleiotropy between blood traits and cancer risk

BACKGROUND

The immune system has a central role in preventing carcinogenesis. Alteration of systemic immune cell levels may increase cancer risk. However, the extent to which common genetic variation influences blood traits and cancer risk remains largely undetermined. Here, we identify pleiotropic variants and predict their underlying molecular and cellular alterations.

METHODS

Multivariate Cox regression was used to evaluate associations between blood traits and cancer diagnosis in cases in the UK Biobank. Shared genetic variants were identified from the summary statistics of the genome-wide association studies of 27 blood traits and 27 cancer types and subtypes, applying the conditional/conjunctional false-discovery rate approach. Analysis of genomic positions, expression quantitative trait loci, enhancers, regulatory marks, functionally defined gene sets, and bulk- and single-cell expression profiles predicted the biological impact of pleiotropic variants. Plasma small RNAs were sequenced to assess association with cancer diagnosis.

RESULTS

The study identified 4093 common genetic variants, involving 1248 gene loci, that contributed to blood-cancer pleiotropism. Genomic hotspots of pleiotropism include chromosomal regions 5p15-TERT and 6p21-HLA. Genes whose products are involved in regulating telomere length are found to be enriched in pleiotropic variants. Pleiotropic gene candidates are frequently linked to transcriptional programs that regulate hematopoiesis and define progenitor cell states of immune system development. Perturbation of the myeloid lineage is indicated by pleiotropic associations with defined master regulators and cell alterations. Eosinophil count is inversely associated with cancer risk. A high frequency of pleiotropic associations is also centered on the regulation of small noncoding Y-RNAs. Predicted pleiotropic Y-RNAs show specific regulatory marks and are overabundant in the normal tissue and blood of cancer patients. Analysis of plasma small RNAs in women who developed breast cancer indicates there is an overabundance of Y-RNA preceding neoplasm diagnosis.

CONCLUSIONS

This study reveals extensive pleiotropism between blood traits and cancer risk. Pleiotropism is linked to factors and processes involved in hematopoietic development and immune system function, including components of the major histocompatibility complexes, and regulators of telomere length and myeloid lineage. Deregulation of Y-RNAs is also associated with pleiotropism. Overexpression of these elements might indicate increased cancer risk.

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.

Single-cell RNA sequencing reveals the dynamics and heterogeneity of lymph node immune cells during acute and chronic viral infections

Introduction

The host immune response determines the differential outcome of acute or chronic viral infections. The comprehensive comparison of lymphoid tissue immune cells at the single-cell level between acute and chronic viral infections is largely insufficient.

Methods

To explore the landscape of immune responses to acute and chronic viral infections, single-cell RNA sequencing(scRNA-seq), scTCR-seq and scBCR-seq were utilized to evaluate the longitudinal dynamics and heterogeneity of lymph node CD45+ immune cells in mouse models of acute (LCMV Armstrong) and chronic (LCMV clone 13) viral infections.

Results

In contrast with acute viral infection, chronic viral infection distinctly induced more robust NK cells and plasma cells at the early stage (Day 4 post-infection) and acute stage (Day 8 post-infection), respectively. Moreover, chronic viral infection exerted decreased but aberrantly activated plasmacytoid dendritic cells (pDCs) at the acute phase. Simultaneously, there were significantly increased IgA+ plasma cells (MALT B cells) but differential usage of B-cell receptors in chronic infection. In terms of T-cell responses, Gzma-high effector-like CD8+ T cells were significantly induced at the early stage in chronic infection, which showed temporally reversed gene expression throughout viral infection and the differential usage of the most dominant TCR clonotype. Chronic infection also induced more robust CD4+ T cell responses, including follicular helper T cells (Tfh) and regulatory T cells (Treg). In addition, chronic infection compromised the TCR diversity in both CD8+ and CD4+ T cells.

Discussion

In conclusion, gene expression and TCR/BCR immune repertoire profiling at the single-cell level in this study provide new insights into the dynamic and differential immune responses to acute and chronic viral infections.

The bone marrow is the primary site of thrombopoiesis

ABSTRACT

Megakaryocytes (MKs) generate thousands of platelets over their lifespan. The roles of platelets in infection and inflammation has guided an interest to the study of extramedullary thrombopoiesis and therefore MKs have been increasingly reported within the spleen and lung. However, the relative abundance of MKs in these organs compared to the bone marrow and the scale of their contribution to the platelet pool in a steady state remain controversial. We investigated the relative abundance of MKs in the adult murine bone marrow, spleen, and lung using whole-mount light-sheet and quantitative histological imaging, flow cytometry, intravital imaging, and an assessment of single-cell RNA sequencing (scRNA-seq) repositories. Flow cytometry revealed significantly higher numbers of hematopoietic stem and progenitor cells and MKs in the murine bone marrow than in spleens or perfused lungs. Two-photon intravital and light-sheet microscopy, as well as quantitative histological imaging, confirmed these findings. Moreover, ex vivo cultured MKs from the bone marrow subjected to static or microfluidic platelet production assays had a higher capacity for proplatelet formation than MKs from other organs. Analysis of previously published murine and human scRNA-seq data sets revealed that only a marginal fraction of MK-like cells can be found within the lung and most likely only marginally contribute to platelet production in the steady state.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

Cytokine-responsive T- and NK-cells portray SARS-CoV-2 vaccine-responders and infection in multiple myeloma patients

Patients with multiple myeloma (MM) routinely receive mRNA-based vaccines to reduce COVID-19-related mortality. However, whether disease- and therapy-related alterations in immune cells and cytokine-responsiveness contribute to the observed heterogeneous vaccination responses is unclear. Thus, we analyzed peripheral blood mononuclear cells from patients with MM during and after SARS-CoV-2 vaccination and breakthrough infection (BTI) using combined whole-transcriptome and surface proteome single-cell profiling with functional serological and T-cell validation in 58 MM patients. Our results demonstrate that vaccine-responders showed a significant overrepresentation of cytotoxic CD4+ T- and mature CD38+ NK-cells expressing FAS+/TIM3+ with a robust cytokine-responsiveness, such as type-I-interferon-, IL-12- and TNF-α-mediated signaling. Patients with MM experiencing BTI developed strong serological and cellular responses and exhibited similar cytokine-responsive immune cell patterns as vaccine-responders. This study can expand our understanding of molecular and cellular patterns associated with immunization responses and may benefit the design of improved vaccination strategies in immunocompromised patients.

Precise single-cell transcriptomic mapping of normal and leukemic cell states reveals unconventional lineage priming in acute myeloid leukemia

Initial classification of acute leukemia involves the assignment of blasts to cell states within the hematopoietic hierarchy based on morphological and immunophenotypic features. Yet, these traditional classification approaches lack precision, especially at the level of immature blasts. Single-cell RNA-sequencing (scRNA-seq) enables precise determination of cell state using thousands of markers, thus providing an opportunity to re-examine present-day classification schemes of acute leukemia. Here, we developed a detailed reference map of human bone marrow hematopoiesis from 263,519 single-cell transcriptomes spanning 55 cellular states. Cell state annotations were benchmarked against purified cell populations, and in-depth characterization of gene expression programs underlying hematopoietic differentiation was undertaken. Projection of single-cell transcriptomes from 175 samples spanning acute myeloid leukemia (AML), mixed phenotype acute leukemia (MPAL), and acute erythroid leukemia (AEL) revealed 11 subtypes involving distinct stages of hematopoietic differentiation. These included AML subtypes with notable lymphoid or erythroid lineage priming, challenging traditional diagnostic boundaries between AML, MPAL, and AEL. Quantification of lineage priming in bulk patient cohorts revealed specific genetic alterations associated with this unconventional lineage priming. Integration of transcriptional and genetic information at the single-cell level revealed how genetic subclones can induce lineage restriction, differentiation blocks, or expansion of mature myeloid cells. Furthermore, we demonstrate that distinct cellular hierarchies can co-exist within individual patients, providing insight into AML evolution in response to varying selection pressures. Together, precise mapping of hematopoietic cell states can serve as a foundation for refining disease classification in acute leukemia and understanding response or resistance to emerging therapies.

SINGLE CELL DISSECTION OF DEVELOPMENTAL ORIGINS AND TRANSCRIPTIONAL HETEROGENEITY IN B-CELL ACUTE LYMPHOBLASTIC LEUKEMIA

Sequencing of bulk tumor populations has improved genetic classification and risk assessment of B-ALL, but does not directly examine intratumor heterogeneity or infer leukemia cellular origins. We profiled 89 B-ALL samples by single-cell RNA-seq (scRNA-seq) and compared them to a reference map of normal human B-cell development established using both functional and molecular assays. Intra-sample heterogeneity was driven by cell cycle, metabolism, differentiation, and inflammation transcriptional programs. By inference of B lineage developmental state composition, nearly all samples possessed a high abundance of pro-B cells, with variation between samples mainly driven by sub-populations. However, ZNF384- r and DUX4- r B-ALL showed composition enrichment of hematopoietic stem cells, BCR::ABL1 and KMT2A -r ALL of Early Lymphoid progenitors, MEF2D -r and TCF3::PBX1 of Pre-B cells. Enrichment of Early Lymphoid progenitors correlated with high-risk clinical features. Understanding variation in transcriptional programs and developmental states of B-ALL by scRNA-seq refines existing clinical and genomic classifications and improves prediction of treatment outcome.

Skull bone marrow channels as immune gateways to the central nervous system

Decades of research have characterized diverse immune cells surveilling the CNS. More recently, the discovery of osseous channels (so-called 'skull channels') connecting the meninges with the skull and vertebral bone marrow has revealed a new layer of complexity in our understanding of neuroimmune interactions. Here we discuss our current understanding of skull and vertebral bone marrow anatomy, its contribution of leukocytes to the meninges, and its surveillance of the CNS. We explore the role of this hematopoietic output on CNS health, focusing on the supply of immune cells during health and disease.

Large-scale single-cell RNA sequencing atlases of human immune cells across lifespan: Possibilities and challenges

Single-cell RNA sequencing technologies have successfully been leveraged for immunological insights into human prenatal, pediatric, and adult tissues. These single-cell studies have led to breakthroughs in our understanding of stem, myeloid, and lymphoid cell function. Computational analysis of fetal hematopoietic tissues has uncovered trajectories for T- and B-cell differentiation across multiple organ sites, and how these trajectories might be dysregulated in fetal and pediatric health and disease. As we enter the age of large-scale, multiomic, and integrative single-cell meta-analysis, we assess the advances and challenges of large-scale data generation, analysis, and reanalysis, and data dissemination for a broad range of scientific and clinical communities. We discuss Findable, Accessible, Interoperable, and Reusable data sharing and unified cell ontology languages as strategic areas for progress of the field in the near future. We also reflect on the trend toward deployment of multiomic and spatial genomic platforms within single-cell RNA sequencing projects, and the crucial role these data types will assume in the immediate future toward creation of comprehensive and rich single-cell atlases. We demonstrate using our recent studies of human prenatal and adult hematopoietic tissues the importance of interdisciplinary and collaborative working in science to reveal biological insights in parallel with technological and computational innovations.

Understanding blood development and leukemia using sequencing-based technologies and human cell systems

Our current understanding of human hematopoiesis has undergone significant transformation throughout the years, challenging conventional views. The evolution of high-throughput technologies has enabled the accumulation of diverse data types, offering new avenues for investigating key regulatory processes in blood cell production and disease. In this review, we will explore the opportunities presented by these advancements for unraveling the molecular mechanisms underlying normal and abnormal hematopoiesis. Specifically, we will focus on the importance of enhancer-associated regulatory networks and highlight the crucial role of enhancer-derived transcription regulation. Additionally, we will discuss the unprecedented power of single-cell methods and the progression in using in vitro human blood differentiation system, in particular induced pluripotent stem cell models, in dissecting hematopoietic processes. Furthermore, we will explore the potential of ever more nuanced patient profiling to allow precision medicine approaches. Ultimately, we advocate for a multiparameter, regulatory network-based approach for providing a more holistic understanding of normal hematopoiesis and blood disorders.

Three-dimensional spatial mapping of the human hematopoietic microenvironment in healthy and diseased bone marrow

The bone marrow hematopoietic microenvironment (HME) plays a pivotal role in regulating normal and diseased hematopoiesis. However, the spatial organization of the human HME has not been thoroughly investigated yet. Therefore, we developed a three-dimensional (3D) immunofluorescence model to analyze changes in the cellular architecture in control and diseased bone marrows (BMs). BM biopsies from patients with myeloproliferative neoplasms (MPNs) were stained sequentially for CD31, CD34, CD45, and CD271 with repetitive bleaching steps to realize five color images with DAPI as a nuclear stain. Hematopoietically normal age-matched BM biopsies served as controls. Twelve subsequent slides per sample were stacked to create three-dimensional bone marrow reconstructions with the imaging program Arivis Visions 4D. Iso-surfaces for niche cells and structures were created and exported as mesh objects for spatial distribution analysis in the 3D creation suite Blender. We recapitulated the bone marrow architecture using this approach and produced comprehensive 3D models of endosteal and perivascular BM niches. MPN bone marrows displayed apparent differences compared to the controls, especially concerning CD271 staining density, megakaryocyte (MK) morphology, and distribution. Furthermore, measurements of the spatial relationships of MKs and hematopoietic stem and progenitor cells with vessels and bone structures in their corresponding niche environments revealed the most pronounced differences in the vascular nice in polycythemia vera. Taken together, using a repetitive staining and bleaching approach allowed us to establish a 5-color analysis of human BM biopsies, which is difficult to achieve with conventional staining approaches. Based on this, we generated 3D BM models which recapitulated key pathological features and, importantly, allowed us to define the spatial relationships between different bone marrow cell types. We, therefore, believe that our method can provide new and valuable insights into bone marrow cellular interaction research.

Single-cell multi-omics defines the cell-type-specific impact of splicing aberrations in human hematopoietic clonal outgrowths

RNA splicing factors are recurrently mutated in clonal blood disorders, but the impact of dysregulated splicing in hematopoiesis remains unclear. To overcome technical limitations, we integrated genotyping of transcriptomes (GoT) with long-read single-cell transcriptomics and proteogenomics for single-cell profiling of transcriptomes, surface proteins, somatic mutations, and RNA splicing (GoT-Splice). We applied GoT-Splice to hematopoietic progenitors from myelodysplastic syndrome (MDS) patients with mutations in the core splicing factor SF3B1. SF3B1mut cells were enriched in the megakaryocytic-erythroid lineage, with expansion of SF3B1mut erythroid progenitor cells. We uncovered distinct cryptic 3' splice site usage in different progenitor populations and stage-specific aberrant splicing during erythroid differentiation. Profiling SF3B1-mutated clonal hematopoiesis samples revealed that erythroid bias and cell-type-specific cryptic 3' splice site usage in SF3B1mut cells precede overt MDS. Collectively, GoT-Splice defines the cell-type-specific impact of somatic mutations on RNA splicing, from early clonal outgrowths to overt neoplasia, directly in human samples.

A Novel Type of Monocytic Leukemia Stem Cell Revealed by the Clinical Use of Venetoclax-Based Therapy

The BCL2 inhibitor venetoclax has recently emerged as an important component of acute myeloid leukemia (AML) therapy. Notably, use of this agent has revealed a previously unrecognized form of pathogenesis characterized by monocytic disease progression. We demonstrate that this form of disease arises from a fundamentally different type of leukemia stem cell (LSC), which we designate as monocytic LSC (m-LSC), that is developmentally and clinically distinct from the more well-described primitive LSC (p-LSC). The m-LSC is distinguished by a unique immunophenotype (CD34-, CD4+, CD11b-, CD14-, CD36-), unique transcriptional state, reliance on purine metabolism, and selective sensitivity to cladribine. Critically, in some instances, m-LSC and p-LSC subtypes can co-reside in the same patient with AML and simultaneously contribute to overall tumor biology. Thus, our findings demonstrate that LSC heterogeneity has direct clinical significance and highlight the need to distinguish and target m-LSCs as a means to improve clinical outcomes with venetoclax-based regimens.

SIGNIFICANCE

These studies identify and characterize a new type of human acute myeloid LSC that is responsible for monocytic disease progression in patients with AML treated with venetoclax-based regimens. Our studies describe the phenotype, molecular properties, and drug sensitivities of this unique LSC subclass. This article is featured in Selected Articles from This Issue, p. 1949.

Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis

Multimodal single-cell technologies profile multiple modalities for each cell simultaneously, enabling a more thorough characterization of cell populations. Existing dimension-reduction methods for multimodal data capture the "union of information," producing a lower-dimensional embedding that combines the information across modalities. While these tools are useful, we focus on a fundamentally different task of separating and quantifying the information among cells that is shared between the two modalities as well as unique to only one modality. Hence, we develop Tilted Canonical Correlation Analysis (Tilted-CCA), a method that decomposes a paired multimodal dataset into three lower-dimensional embeddings-one embedding captures the "intersection of information," representing the geometric relations among the cells that is common to both modalities, while the remaining two embeddings capture the "distinct information for a modality," representing the modality-specific geometric relations. We analyze single-cell multimodal datasets sequencing RNA along surface antibodies (i.e., CITE-seq) as well as RNA alongside chromatin accessibility (i.e., 10x) for blood cells and developing neurons via Tilted-CCA. These analyses show that Tilted-CCA enables meaningful visualization and quantification of the cross-modal information. Finally, Tilted-CCA's framework allows us to perform two specific downstream analyses. First, for single-cell datasets that simultaneously profile transcriptome and surface antibody markers, we show that Tilted-CCA helps design the target antibody panel to complement the transcriptome best. Second, for developmental single-cell datasets that simultaneously profile transcriptome and chromatin accessibility, we show that Tilted-CCA helps identify development-informative genes and distinguish between transient versus terminal cell types.

Treatment of adult ALL patients with third-generation CD19-directed CAR T cells: results of a pivotal trial

BACKGROUND

Third-generation chimeric antigen receptor (CAR)-engineered T cells (CARTs) might improve clinical outcome of patients with B cell malignancies. This is the first report on a third-generation CART dose-escalating, phase-1/2 investigator-initiated trial treating adult patients with refractory and/or relapsed (r/r) acute lymphoblastic leukemia (ALL).

METHODS

Thirteen patients were treated with escalating doses of CD19-directed CARTs between 1 × 10⁶ and 50 × 10⁶ CARTs/m². Leukapheresis, manufacturing and administration of CARTs were performed in-house.

RESULTS

For all patients, CART manufacturing was feasible. None of the patients developed any grade of Immune effector cell-associated neurotoxicity syndrome (ICANS) or a higher-grade (≥ grade III) catokine release syndrome (CRS). CART expansion and long-term CART persistence were evident in the peripheral blood (PB) of evaluable patients. At end of study on day 90 after CARTs, ten patients were evaluable for response: Eight patients (80%) achieved a complete remission (CR), including five patients (50%) with minimal residual disease (MRD)-negative CR. Response and outcome were associated with the administered CART dose. At 1-year follow-up, median overall survival was not reached and progression-free survival (PFS) was 38%. Median PFS was reached on day 120. Lack of CD39-expression on memory-like T cells was more frequent in CART products of responders when compared to CART products of non-responders. After CART administration, higher CD8 + and γδ-T cell frequencies, a physiological pattern of immune cells and lower monocyte counts in the PB were associated with response.

CONCLUSION

In conclusion, third-generation CARTs were associated with promising clinical efficacy and remarkably low procedure-specific toxicity, thereby opening new therapeutic perspectives for patients with r/r ALL. Trial registration This trial was registered at www.

CLINICALTRIALS

gov as NCT03676504.

Transcriptomic classes of BCR-ABL1 lymphoblastic leukemia

In BCR-ABL1 lymphoblastic leukemia, treatment heterogeneity to tyrosine kinase inhibitors (TKIs), especially in the absence of kinase domain mutations in BCR-ABL1, is poorly understood. Through deep molecular profiling, we uncovered three transcriptomic subtypes of BCR-ABL1 lymphoblastic leukemia, each representing a maturation arrest at a stage of B-cell progenitor differentiation. An earlier arrest was associated with lineage promiscuity, treatment refractoriness and poor patient outcomes. A later arrest was associated with lineage fidelity, durable leukemia remissions and improved patient outcomes. Each maturation arrest was marked by specific genomic events that control different transition points in B-cell development. Interestingly, these events were absent in BCR-ABL1⁺ preleukemic stem cells isolated from patients regardless of subtype, which supports that transcriptomic phenotypes are determined downstream of the leukemia-initialing event. Overall, our data indicate that treatment response and TKI efficacy are unexpected outcomes of the differentiation stage at which this leukemia transforms.

Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia

The bone marrow (BM) is a complex microenvironment, coordinating the production of billions of blood cells every day. Despite its essential role and its relevance to hematopoietic diseases, this environment remains poorly characterized. Here we present a high-resolution characterization of the niche in health and acute myeloid leukemia (AML) by establishing a single-cell gene expression database of 339,381 BM cells. We found significant changes in cell type proportions and gene expression in AML, indicating that the entire niche is disrupted. We then predicted interactions between hematopoietic stem and progenitor cells (HSPCs) and other BM cell types, revealing a remarkable expansion of predicted interactions in AML that promote HSPC-cell adhesion, immunosuppression, and cytokine signaling. In particular, predicted interactions involving transforming growth factor β1 (TGFB1) become widespread, and we show that this can drive AML cell quiescence in vitro. Our results highlight potential mechanisms of enhanced AML-HSPC competitiveness and a skewed microenvironment, fostering AML growth.

Hematopoietic stem cells and betaherpesvirus latency

The human betaherpesviruses including human cytomegalovirus (HCMV), human herpesvirus (HHV)-6a and HHV-6b, and HHV-7 infect and establish latency in CD34+ hematopoietic stem and progenitor cells (HPCs). The diverse repertoire of HPCs in humans and the complex interactions between these viruses and host HPCs regulate the viral lifecycle, including latency. Precise manipulation of host and viral factors contribute to preferential maintenance of the viral genome, increased host cell survival, and specific manipulation of the cellular environment including suppression of neighboring cells and immune control. The dynamic control of these processes by the virus regulate inter- and intra-host signals critical to the establishment of chronic infection. Regulation occurs through direct viral protein interactions and cellular signaling, miRNA regulation, and viral mimics of cellular receptors and ligands, all leading to control of cell proliferation, survival, and differentiation. Hematopoietic stem cells have unique biological properties and the tandem control of virus and host make this a unique environment for chronic herpesvirus infection in the bone marrow. This review highlights the elegant complexities of the betaherpesvirus latency and HPC virus-host interactions.

Selective homing of CAR-CIK cells to the bone marrow niche enhances control of the acute myeloid leukemia burden

Acute myeloid leukemia (AML) is a hematological malignancy derived from neoplastic myeloid progenitor cells characterized by abnormal clonal proliferation and differentiation. Although novel therapeutic strategies have recently been introduced, the prognosis of AML is still unsatisfactory. So far, the efficacy of chimeric antigen receptor (CAR)-T-cell therapy in AML has been hampered by several factors, including the poor accumulation of the blood-injected cells in the leukemia bone marrow (BM) niche in which chemotherapy-resistant leukemic stem cells reside. Thus, we hypothesized that overexpression of CXCR4, whose ligand CXCL12 is highly expressed by BM stromal cells within this niche, could improve T-cell homing to the BM and consequently enhance their intimate contact with BM-resident AML cells, facilitating disease eradication. Specifically, we engineered conventional CD33.CAR-cytokine-induced killer cells (CIKs) with the wild-type (wt) CXCR4 and the variant CXCR4R334X, responsible for leukocyte sequestration in the BM of patients with warts, hypogammaglobulinemia, immunodeficiency, and myelokathexis syndrome. Overexpression of both CXCR4wt and CXCR4mut in CD33.CAR-CIKs resulted in significant improvement of chemotaxis toward recombinant CXCL12 or BM stromal cell-conditioned medium, with no observed impairment of cytotoxic potential in vitro. Moreover, CXCR4-overexpressing CD33.CAR-CIKs showed enhanced in vivo BM homing, associated with a prolonged retention for the CXCR4R334X variant. However, only CD33.CAR-CIKs coexpressing CXCR4wt but not CXCR4mut exerted a more sustained in vivo antileukemic activity and extended animal survival, suggesting a noncanonical role for CXCR4 in modulating CAR-CIK functions independent of BM homing. Taken together, these data suggest that arming CAR-CIKs with CXCR4 may represent a promising strategy for increasing their therapeutic potential for AML.

Delivery of PEGylated liposomal doxorubicin by bispecific antibodies improves treatment in models of high-risk childhood leukemia

High-risk childhood leukemia has a poor prognosis because of treatment failure and toxic side effects of therapy. Drug encapsulation into liposomal nanocarriers has shown clinical success at improving biodistribution and tolerability of chemotherapy. However, enhancements in drug efficacy have been limited because of a lack of selectivity of the liposomal formulations for the cancer cells. Here, we report on the generation of bispecific antibodies (BsAbs) with dual binding to a leukemic cell receptor, such as CD19, CD20, CD22, or CD38, and methoxy polyethylene glycol (PEG) for the targeted delivery of PEGylated liposomal drugs to leukemia cells. This liposome targeting system follows a "mix-and-match" principle where BsAbs were selected on the specific receptors expressed on leukemia cells. BsAbs improved the targeting and cytotoxic activity of a clinically approved and low-toxic PEGylated liposomal formulation of doxorubicin (Caelyx) toward leukemia cell lines and patient-derived samples that are immunophenotypically heterogeneous and representative of high-risk subtypes of childhood leukemia. BsAb-assisted improvements in leukemia cell targeting and cytotoxic potency of Caelyx correlated with receptor expression and were minimally detrimental in vitro and in vivo toward expansion and functionality of normal peripheral blood mononuclear cells and hematopoietic progenitors. Targeted delivery of Caelyx using BsAbs further enhanced leukemia suppression while reducing drug accumulation in the heart and kidneys and extended overall survival in patient-derived xenograft models of high-risk childhood leukemia. Our methodology using BsAbs therefore represents an attractive targeting platform to potentiate the therapeutic efficacy and safety of liposomal drugs for improved treatment of high-risk leukemia.

pyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells

MOTIVATION

While conventional flow cytometry is limited to dozens of markers, new experimental and computational strategies, such as Infinity Flow, allow for the generation and imputation of hundreds of cell surface protein markers in millions of cells. Here, we describe an end-to-end analysis workflow for Infinity Flow data in Python.

RESULTS

pyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established Python packages for single-cell genomics analysis. pyInfinityFlow accurately identifies both common and extremely rare cell populations which are challenging to define from single-cell genomics studies alone. We demonstrate that this workflow can nominate novel markers to design new flow cytometry gating strategies for predicted cell populations. pyInfinityFlow can be extended to diverse cell discovery analyses with flexibility to adapt to diverse Infinity Flow experimental designs.

AVAILABILITY AND IMPLEMENTATION

pyInfinityFlow is freely available in GitHub (https://github.com/KyleFerchen/pyInfinityFlow) and on PyPI (https://pypi.org/project/pyInfinityFlow/). Package documentation with tutorials on a test dataset is available by Read the Docs (pyinfinityflow.readthedocs.io). The scripts and data for reproducing the results are available at https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analysis_scripts, along with the raw flow cytometry input data.

Ultra High-plex Spatial Proteogenomic Investigation of Giant Cell Glioblastoma Multiforme Immune Infiltrates Reveals Distinct Protein and RNA Expression Profiles

A deeper understanding of complex biological processes, including tumor development and immune response, requires ultra high-plex, spatial interrogation of multiple "omes". Here we present the development and implementation of a novel spatial proteogenomic (SPG) assay on the GeoMx Digital Spatial Profiler platform with next-generation sequencing readout that enables ultra high-plex digital quantitation of proteins (>100-plex) and RNA (whole transcriptome, >18,000-plex) from a single formalin-fixed paraffin-embedded (FFPE) sample. This study highlighted the high concordance, R > 0.85 and <15% change in sensitivity between the SPG assay and the single-analyte assays on various cell lines and tissues from human and mouse. Furthermore, we demonstrate that the SPG assay was reproducible across multiple users. When used in conjunction with advanced cellular neighborhood segmentation, distinct immune or tumor RNA and protein targets were spatially resolved within individual cell subpopulations in human colorectal cancer and non-small cell lung cancer. We used the SPG assay to interrogate 23 different glioblastoma multiforme (GBM) samples across four pathologies. The study revealed distinct clustering of both RNA and protein based on pathology and anatomic location. The in-depth investigation of giant cell glioblastoma multiforme (gcGBM) revealed distinct protein and RNA expression profiles compared with that of the more common GBM. More importantly, the use of spatial proteogenomics allowed simultaneous interrogation of critical protein posttranslational modifications alongside whole transcriptomic profiles within the same distinct cellular neighborhoods.

Significance

We describe ultra high-plex spatial proteogenomics; profiling whole transcriptome and high-plex proteomics on a single FFPE tissue section with spatial resolution. Investigation of gcGBM versus GBM revealed distinct protein and RNA expression profiles.

Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia

Inter-patient variability and the similarity of healthy and leukemic stem cells (LSCs) have impeded the characterization of LSCs in acute myeloid leukemia (AML) and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. Although residual healthy and preleukemic cells dominated the dormant stem cell compartment, active LSCs resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors constituted a highly aberrant, disease-defining compartment: their gene expression and differentiation state affected both the chemotherapy response and leukemia's ability to differentiate into transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers misregulated specifically in leukemic cells. Taken together, CloneTracer reveals a differentiation landscape that mimics its healthy counterpart and may determine biology and therapy response in AML.

M2 macrophages drive leukemic transformation by imposing resistance to phagocytosis and improving mitochondrial metabolism

It is increasingly becoming clear that cancers are a symbiosis of diverse cell types and tumor clones. Combined single-cell RNA sequencing, flow cytometry, and immunohistochemistry studies of the innate immune compartment in the bone marrow of patients with acute myeloid leukemia (AML) reveal a shift toward a tumor-supportive M2-polarized macrophage landscape with an altered transcriptional program, with enhanced fatty acid oxidation and NAD⁺ generation. Functionally, these AML-associated macrophages display decreased phagocytic activity and intra-bone marrow coinjection of M2 macrophages together with leukemic blasts strongly enhances in vivo transformation potential. A 2-day in vitro exposure to M2 macrophages results in the accumulation of CALR^low leukemic blast cells, which are now protected against phagocytosis. Moreover, M2-exposed "trained" leukemic blasts display increased mitochondrial metabolism, in part mediated via mitochondrial transfer. Our study provides insight into the mechanisms by which the immune landscape contributes to aggressive leukemia development and provides alternatives for targeting strategies aimed at the tumor microenvironment.