Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

Immune Cell-NSPC interactions: Friend or foe in CNS injury and repair?

Neural stem/progenitor cells (NSPCs) play a crucial role in central nervous system (CNS) development, regeneration, and repair. However, their functionality and therapeutic potential are intricately modulated by interactions with immune cells, particularly macrophages and microglia. Microglia, as CNS-resident macrophages, are distinct from peripheral macrophages in their roles and characteristics, contributing to specialized functions within the CNS. Recent evidence suggests that microglia, as CNS-resident macrophages, contribute to the quality assurance of NSPCs by eliminating stressed or dysfunctional cells, yet the mechanisms underlying this process remain largely unexplored. Furthermore, macrophage polarization states, such as M1 and M2, appear to differentially influence NSPC quality, potentially impacting neurogenesis and regenerative outcomes. Identifying surface markers indicative of NSPC stress could provide a strategy for selecting optimal cells for transplantation therapies. Additionally, in vivo clonal labeling approaches may enable precise tracking of NSPC fate and their interactions with immune cells. Beyond macrophages and microglia, the roles of other immune cells, including T cells and neutrophils, particularly in injury and neurodegenerative disease contexts, in the context of CNS injury and disease are emerging areas of interest. Here, I discuss the emerging evidence supporting the interplay between the immune system and NSPCs, highlighting critical gaps in knowledge and proposing future research directions to harness immune-mediated mechanisms for optimizing neural regeneration and transplantation strategies.

MitoSort: Robust Demultiplexing of Pooled Single-cell Genomic Data Using Endogenous Mitochondrial Variants

Multiplexing across donors has emerged as a popular strategy to increase throughput, reduce costs, overcome technical batch effects, and improve doublet detection in single-cell genomic studies. To eliminate additional experimental steps, endogenous nuclear genome variants are used for demultiplexing pooled single-cell RNA sequencing (scRNA-seq) data by several computational tools. However, these tools have limitations when applied to single-cell sequencing methods that do not cover nuclear genomic regions well, such as single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq). Here, we demonstrate that mitochondrial germline variants are an alternative, robust, and computationally efficient endogenous barcode for sample demultiplexing. We propose MitoSort, a tool that uses mitochondrial germline variants to assign cells to their donor origins and identify cross-genotype doublets in single-cell genomic datasets. We evaluate its performance by using in silico pooled mitochondrial scATAC-seq (mtscATAC-seq) libraries and experimentally multiplexed data with cell hashtags. MitoSort achieves high accuracy and efficiency in genotype clustering and doublet detection for mtscATAC-seq data, addressing the limitations of current computational techniques tailored for scRNA-seq data. Moreover, MitoSort exhibits versatility, and can be applied to various single-cell sequencing approaches beyond mtscATAC-seq provided that the mitochondrial variants are reliably detected. Furthermore, we demonstrate the application of MitoSort in a case study where B cells from eight donors were pooled and assayed by single-cell multi-omics sequencing. Altogether, our results demonstrate the accuracy and efficiency of MitoSort, which enables reliable sample demultiplexing in various single-cell genomic applications. MitoSort is available at https://github.com/tangzhj/MitoSort.

Tracking Rare Single Donor and Recipient Immune and Leukemia Cells after Allogeneic Hematopoietic Cell Transplantation Using Mitochondrial DNA Mutations

Combined tracking of clonal evolution and chimeric cell phenotypes could enable detection of the key cellular populations associated with response following therapy, including after allogeneic hematopoietic stem cell transplantation (HSCT). We demonstrate that mitochondrial DNA (mtDNA) mutations coevolve with somatic nuclear DNA mutations at relapse post-HSCT and provide a sensitive means to monitor these cellular populations. Furthermore, detection of mtDNA mutations via single-cell assay for transposase-accessible chromatin with select antigen profiling by sequencing (ASAP-seq) simultaneously determines not only donor and recipient cells but also their phenotype at frequencies of 0.1% to 1%. Finally, integration of mtDNA mutations, surface markers, and chromatin accessibility profiles enables the phenotypic resolution of leukemic populations from normal immune cells, thereby providing fresh insights into residual donor-derived engraftment and short-term clonal evolution following therapy for post-transplant leukemia relapse. As throughput evolves, we envision future development of single-cell sequencing-based post-transplant monitoring as a powerful approach for guiding clinical decision-making. Significance: mtDNA mutations enable single-cell tracking of leukemic clonal evolution and donor-recipient origin following allogeneic HSCT. This provides unprecedented insight into chimeric cellular phenotypes of early immune reconstitution, incipient relapse, and quality of donor engraftment with immediate translational potential for future clinical post-transplant monitoring and decision-making.

Mitochondrial genetic variations in leukemia: a comprehensive overview

Leukemias are a group of heterogeneous hematological malignancies driven by diverse genetic variations, and the advent of genomic sequencing technologies facilitates the investigation of genetic abnormalities in leukemia. However, these sequencing-based studies mainly focus on nuclear DNAs. Increasing evidence indicates that mitochondrial dysfunction is an important mechanism of leukemia pathogenesis, which is closely related to the mitochondrial genome variations. Here, we provide an overview of current research progress concerning mitochondrial genetic variations in leukemia, encompassing gene mutations and copy number variations. We also summarize currently accessible mitochondrial DNA (mtDNA) sequencing methods. Notably, somatic mtDNA mutations may serve as natural genetic barcodes for lineage tracing and longitudinal assessment of clonal dynamics. Collectively, these findings enhance our understanding of leukemia pathogenesis and foster the identification of novel therapeutic targets and interventions.

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.

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.

Single-cell lineage tracing with endogenous markers

Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.

Integrative genotyping of cancer and immune phenotypes by long-read sequencing

Single-cell transcriptomics has become the definitive method for classifying cell types and states, and can be augmented with genotype information to improve cell lineage identification. Due to constraints of short-read sequencing, current methods to detect natural genetic barcodes often require cumbersome primer panels and early commitment to targets. Here we devise a flexible long-read sequencing workflow and analysis pipeline, termed nanoranger, that starts from intermediate single-cell cDNA libraries to detect cell lineage-defining features, including single-nucleotide variants, fusion genes, isoforms, sequences of chimeric antigen and TCRs. Through systematic analysis of these classes of natural 'barcodes', we define the optimal targets for nanoranger, namely those loci close to the 5' end of highly expressed genes with transcript lengths shorter than 4 kB. As proof-of-concept, we apply nanoranger to longitudinal tracking of subclones of acute myeloid leukemia (AML) and describe the heterogeneous isoform landscape of thousands of marrow-infiltrating immune cells. We propose that enhanced cellular genotyping using nanoranger can improve the tracking of single-cell tumor and immune cell co-evolution.

Cancer cell plasticity during tumor progression, metastasis and response to therapy

Cell plasticity represents the ability of cells to be reprogrammed and to change their fate and identity, enabling homeostasis restoration and tissue regeneration following damage. Cell plasticity also contributes to pathological conditions, such as cancer, enabling cells to acquire new phenotypic and functional features by transiting across distinct cell states that contribute to tumor initiation, progression, metastasis and resistance to therapy. Here, we review the intrinsic and extrinsic mechanisms driving cell plasticity that promote tumor growth and proliferation as well as metastasis and drug tolerance. Finally, we discuss how cell plasticity could be exploited for anti-cancer therapy.

Cellular barcoding tracks heterogeneous clones through selective pressures and phenotypic transitions

Genomic DNA barcoding has emerged as a sensitive and flexible tool to measure the fates of clonal subpopulations within a heterogeneous cancer cell population. Coupling cellular barcoding with single-cell transcriptomics permits the longitudinal analysis of molecular mechanisms with detailed clone-level resolution. Numerous recent studies have employed these tools to track clonal cell states in cancer progression and treatment response. With these new technologies comes the opportunity to examine longstanding questions about the origins and contributions of tumor cell heterogeneity and the roles of selection and phenotypic plasticity in disease progression and treatment.

Dynamics and specificities of T cells in cancer immunotherapy

Recent advances in cancer immunotherapy - ranging from immune-checkpoint blockade therapy to adoptive cellular therapy and vaccines - have revolutionized cancer treatment paradigms, yet the variability in clinical responses to these agents has motivated intense interest in understanding how the T cell landscape evolves with respect to response to immune intervention. Over the past decade, the advent of multidimensional single-cell technologies has provided the unprecedented ability to dissect the constellation of cell states of lymphocytes within a tumour microenvironment. In particular, the rapidly expanding capacity to definitively link intratumoural phenotypes with the antigen specificity of T cells provided by T cell receptors (TCRs) has now made it possible to focus on investigating the properties of T cells with tumour-specific reactivity. Moreover, the assessment of TCR clonality has enabled a molecular approach to track the trajectories, clonal dynamics and phenotypic changes of antitumour T cells over the course of immunotherapeutic intervention. Here, we review the current knowledge on the cellular states and antigen specificities of antitumour T cells and examine how fine characterization of T cell dynamics in patients has provided meaningful insights into the mechanisms underlying effective cancer immunotherapy. We highlight those T cell subsets associated with productive T cell responses and discuss how diverse immunotherapies might leverage the pre-existing tumour-reactive T cell pool or instruct de novo generation of antitumour specificities. Future studies aimed at elucidating the factors associated with the elicitation of productive antitumour T cell immunity are anticipated to instruct the design of more efficacious treatment strategies.

Mechanisms of response and resistance to combined decitabine and ipilimumab for advanced myeloid disease

The challenge of eradicating leukemia in patients with acute myelogenous leukemia (AML) after initial cytoreduction has motivated modern efforts to combine synergistic active modalities including immunotherapy. Recently, the ETCTN/CTEP 10026 study tested the combination of the DNA methyltransferase inhibitor decitabine together with the immune checkpoint inhibitor ipilimumab for AML/myelodysplastic syndrome (MDS) either after allogeneic hematopoietic stem cell transplantation (HSCT) or in the HSCT-naïve setting. Integrative transcriptome-based analysis of 304 961 individual marrow-infiltrating cells for 18 of 48 subjects treated on study revealed the strong association of response with a high baseline ratio of T to AML cells. Clinical responses were predominantly driven by decitabine-induced cytoreduction. Evidence of immune activation was only apparent after ipilimumab exposure, which altered CD4+ T-cell gene expression, in line with ongoing T-cell differentiation and increased frequency of marrow-infiltrating regulatory T cells. For post-HSCT samples, relapse could be attributed to insufficient clearing of malignant clones in progenitor cell populations. In contrast to AML/MDS bone marrow, the transcriptomes of leukemia cutis samples from patients with durable remission after ipilimumab monotherapy showed evidence of increased infiltration with antigen-experienced resident memory T cells and higher expression of CTLA-4 and FOXP3. Altogether, activity of combined decitabine and ipilimumab is impacted by cellular expression states within the microenvironmental niche of leukemic cells. The inadequate elimination of leukemic progenitors mandates urgent development of novel approaches for targeting these cell populations to generate long-lasting responses. This trial was registered at www.clinicaltrials.gov as #NCT02890329.

Mitochondrial DNA Mutations as Natural Barcodes for Lineage Tracing of Murine Tumor Models

Murine models are indispensable tools for functional genomic studies and preclinical testing of novel therapeutic approaches. Mitochondrial single-cell assay for transposase-accessible chromatin using sequencing (mtscATAC-seq) enables the dissection of cellular heterogeneity and clonal dynamics by capturing chromatin accessibility, copy-number variations (CNV), and mitochondrial DNA (mtDNA) mutations, yet its applicability to murine studies remains unexplored. By leveraging mtscATAC-seq in novel chronic lymphocytic leukemia and Richter syndrome mouse models, we report the detection of mtDNA mutations, particularly in highly proliferative murine cells, alongside CNV and chromatin state changes indicative of clonal evolution upon secondary transplant. This study thus demonstrates the feasibility and utility of multi-modal single-cell and natural barcoding approaches to characterize murine cancer models.

SIGNIFICANCE

mtDNA mutations can serve as natural barcodes to enable lineage tracing in murine cancer models, which can be used to provide new insights into disease biology and to identify therapeutic vulnerabilities.

The end of the beginning: application of single-cell sequencing to chronic lymphocytic leukemia

Single-cell analysis has emerged over the past decade as a transformative technology informative for the systematic analysis of complex cell populations such as in cancers and the tumor immune microenvironment. The methodologic and analytical advancements in this realm have evolved rapidly, scaling from but a few cells at its outset to the current capabilities of processing and analyzing hundreds of thousands of individual cells at a time. The types of profiling attainable at individual cell resolution now range from genetic and transcriptomic characterization and extend to epigenomic and spatial analysis. Additionally, the increasing ability to achieve multiomic integration of these data layers now yields ever richer insights into diverse molecular disease subtypes and the patterns of cellular circuitry on a per-cancer basis. Over the years, chronic lymphocytic leukemia (CLL) consistently has been at the forefront of genomic investigation, given the ready accessibility of pure leukemia cells and immune cells from circulating blood of patients with this disease. Herein, we review the recent forays into the application of single-cell analysis to CLL, which are already revealing a new understanding of the natural progression of CLL, the impact of novel therapies, and the interactions with coevolving nonmalignant immune cell populations. As we emerge from the end of the beginning of this technologic revolution, CLL stands poised to reap the benefits of single-cell analysis from the standpoints of uncovering fresh fundamental biological knowledge and of providing a path to devising regimens of personalized diagnosis, treatment, and monitoring.