Joint profiling of DNA and proteins in single cells to dissect genotype-phenotype associations in leukemia

Paired Analyses of Nuclear Protein Targets and Genomic DNA by Single-Cell Western Blot and Single-Cell PCR

Single-cell multimodal assays measure multiple layers of molecular information. Existing single-cell tools have limited capability to analyze nuclear proteins and genomic DNA from the same originating single cell. To address this gap, we designed and developed a microfluidic single-cell assay (SplitBlot), that pairs measurements of genomic DNA (PCR-based) and nucleo-cytoplasmic proteins (nuclear histone H3 and cytoplasmic beta-actin). To accomplish this paired multiomic measurement, we utilize microfluidic precision to fractionate protein molecules (both nuclear and cytoplasmic) from genomic DNA (nuclear). We create a fractionation axis that prepends a comet-like encapsulation of genomic DNA in an agarose molded microwell to a downstream single-cell western blot in polyacrylamide gel (PAG). For single-cell genomic DNA analysis, the agarose-encapsulated DNA is physically extracted from the microfluidic device for in-tube PCR, after release of genomic DNA from a molten agarose pallet (86% of pallets resulted in amplification of TurboGFP). For protein analysis, nucleo-cytoplasmic proteins are photocaptured to the PAG (via benzophenone) and probed in-situ (15 kDa histone H3 resolved from 42 kDa beta-actin with a separation resolution R s = 0.77, CV = 76%). The SplitBlot reported the amplification of TurboGFP DNA and the separation of nuclear histone H3 and cytoplasmic beta-actin from the same single U251 cells engineered to express TurboGFP. Demonstrated here, Split-Blot offers the capacity for precision genomic DNA vs. protein fractionation for subsequent split workflow consisting of in-tube PCR and on-chip single-cell western blotting, thus providing a tool for pairing genotype to nuclear and cytoplasmic protein expression at the single-cell level.

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Role of clonal inflammatory microglia in histiocytosis-associated neurodegeneration

Langerhans cell histiocytosis (LCH) and Erdheim-Chester disease (ECD) are clonal myeloid disorders associated with mitogen-activated protein (MAP)-kinase-activating mutations and an increased risk of neurodegeneration. We found microglial mutant clones in LCH and ECD patients, whether or not they presented with clinical symptoms of neurodegeneration, associated with microgliosis, astrocytosis, and neuronal loss, predominantly in the rhombencephalon gray nuclei. Neurological symptoms were associated with PU.1+ clone size (p = 0.0003) in patients with the longest evolution of the disease, indicating a phase of subclinical incipient neurodegeneration. Genetic barcoding analysis suggests that clones may originate from definitive or yolk sac hematopoiesis, depending on the patients. In a mouse model, disease topography was attributable to a local clonal proliferative advantage, and microglia depletion by a CSF1R-inhibitor limited neuronal loss and improved survival. These studies characterize a neurodegenerative disease associated with clonal proliferation of inflammatory microglia. The long preclinical stage represents a therapeutic window before irreversible neuronal depletion.

Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Single-cell sequencing applications in acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous group of malignancies with poor prognosis. AML result from the proliferation of immature myeloid cells blocked at a variable stage of differentiation. Beyond inter-patient heterogeneity, AMLs are characterized by genetic and phenotypic intra-patient heterogeneity. Despite major advances in deciphering AML biology with bulk sequencing studies, pivotal questions remain unanswered. Analyses at the single-cell level could thus transform our understanding of these neoplasms. We review recent progresses in single-cell sequencing technologies from cell processing to bioinformatic pipelines. We next discuss how single-cell applications have helped understand the genetic and functional intra-leukemic heterogeneity, emphasizing aspects related to leukemic stem cells, clonal evolution and measurable residual disease (MRD) monitoring. We finally delineate how single-cell technologies could be implemented in routine clinical practice to improve patient management.

Unlocking the Heterogeneity in Acute Leukaemia: Dissection of Clonal Architecture and Metabolic Properties for Clinical Interventions

Genetic studies of haematological cancers have pointed out the heterogeneity of leukaemia in its different subpopulations, with distinct mutations and characteristics, impacting the treatment response. Next-generation sequencing (NGS) and genome-wide analyses, as well as single-cell technologies, have offered unprecedented insights into the clonal heterogeneity within the same tumour. A key component of this heterogeneity that remains unexplored is the intracellular metabolome, a dynamic network that determines cell functions, signalling, epigenome regulation, immunity and inflammation. Understanding the metabolic diversities among cancer cells and their surrounding environments is therefore essential in unravelling the complexities of leukaemia and improving therapeutic strategies. Here, we describe the currently available methodologies and approaches to addressing the dynamic heterogeneity of leukaemia progression. In the second section, we focus on metabolic leukaemic vulnerabilities in acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL). Lastly, we provide a comprehensive overview of the most interesting clinical trials designed to target these metabolic dependencies, highlighting their potential to advance therapeutic strategies in leukaemia treatment. The integration of multi-omics data for cancer identification with the metabolic states of tumour cells will enable a comprehensive "micro-to-macro" approach for the refinement of clinical practices and delivery of personalised therapies.

Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. ALL originates from precursor lymphocytes that acquire multiple genomic changes over time, including chromosomal rearrangements and point mutations. While a large variety of genomic defects was identified and characterized in ALL over the past 30 years, it was only in recent years that the clonal heterogeneity was recognized. Thanks to the latest advancements in single-cell sequencing techniques, which have evolved from the analysis of a few hundred cells to the analysis of thousands of cells simultaneously, the study of tumor heterogeneity now becomes possible. Different modalities can be explored at the single-cell level: DNA, RNA, epigenetic modifications, and intracellular and cell surface proteins. In this review, we describe these techniques and highlight their advantages and limitations in the study of ALL biology. Moreover, multiomics technologies and the incorporation of the spatial dimension can provide insight into intercellular communication. We describe how the different single-cell sequencing technologies help to unravel the molecular complexity of ALL, shedding light on its development, its heterogeneity, its interaction with the leukemia microenvironment and possible relapse mechanisms.

Multiomic single cell sequencing identifies stemlike nature of mixed phenotype acute leukemia

Despite recent work linking mixed phenotype acute leukemia (MPAL) to certain genetic lesions, specific driver mutations remain undefined for a significant proportion of patients and no genetic subtype is predictive of clinical outcomes. Moreover, therapeutic strategy for MPAL remains unclear, and prognosis is overall poor. We performed multiomic single cell profiling of 14 newly diagnosed adult MPAL patients to characterize the inter- and intra-tumoral transcriptional, immunophenotypic, and genetic landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. Despite this, we find that MPAL blasts express a shared stem cell-like transcriptional profile indicative of high differentiation potential. Patients with the highest differentiation potential demonstrate inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in the most stem-like MPAL cells, is applicable to bulk RNA sequencing data and is predictive of survival in an independent patient cohort, suggesting a potential strategy for clinical risk stratification.

A self-catalyzing strategy for co-immobilization of two distinct proteins at equimolar ratio: A case study of 3A and 2C to develop a chromatographic method for finding prospective dual-target compoundsfrom complex matrices

BACKGROUND

Immobilized proteins hold promise as the basic units that have enabled a broad range of analytical applications within chemical measurement science. As yet, the co-immobilization of diverse proteins at precise ratio and whether they give rise to improved analytical performance remain challengeable. Herein, we utilized a circularly permuted HaloTag (cpHaloTag) to achieve the co-immobilization of two proteins at precise ratio, which was applied in developing a chromatographic method with improved specificity for pursuing dual-target compounds.

RESULTS

The methodology involved the fusion 3A and 2C at N- and C-terminuses of cpHaloTag, the immobilization of the fusion protein onto silica gel through bioorthogonal reaction, the morphological and functional characterization, the application in finding dual-target compounds. Expression of the fusion protein in E. coli system showed a yield of milligram level with the presence of 3A and 2C domains. Immobilization of the protein was achieved in 10 min with a reaction efficiency more than 88.5 %. Immobilized 3A-cpHalo-2C exhibited higher specificity and better retentions of canonical compounds of the two enzymes in comparison with the column containing immobilized 3A or 2C alone. In real sample application, screening analysis found that hyperoside, cymaroside, and baicalin were dual-target compounds in concert with 3A and 2C in Shuanghuanglian extract.

SIGNIFICANCE

Taking 3A and 2C as probe, we proposed a simple method for direct co-immobilization of diverse proteins from cell lysates and demonstrated an affinity chromatographic-based dual-target compound screening platform. The implications of these methodology are possible to insight the de novo design of multi-target surface for fabricating new bioanalytical methods with improved performance.

Assessing a single-cell multi-omic analytic platform to characterize ex vivo-engineered T-cell therapy products

Genetically engineered CD8+ T cells are being explored for the treatment of various cancers. Analytical characterization represents a major challenge in the development of genetically engineered cell therapies, especially assessing the potential off-target editing and product heterogeneity. As conventional sequencing techniques only provide information at the bulk level, they are unable to detect off-target CRISPR translocation or editing events occurring in minor cell subpopulations. In this study, we report the analytical development of a single-cell multi-omics DNA and protein assay to characterize genetically engineered cell products for safety and genotoxicity assessment. We were able to quantify on-target edits, off-target events, and potential translocations at the targeting loci with per-cell granularity, providing important characterization data of the final cell product. Conclusion: A single-cell multi-omics approach provides the resolution required to understand the composition of cellular products and identify critical quality attributes (CQAs).

Mechanism of neurodegeneration mediated by clonal inflammatory microglia

Langerhans cell Histiocytosis (LCH) and Erdheim-Chester disease (ECD) are clonal myeloid disorders, associated with MAP-Kinase activating mutations and an increased risk of neurodegeneration. Surprisingly, we found pervasive PU.1+ microglia mutant clones across the brain of LCH and ECD patients with and without neurological symptoms, associated with microgliosis, reactive astrocytosis, and neuronal loss. The disease predominated in the grey nuclei of the rhombencephalon, a topography attributable to a local proliferative advantage of mutant microglia. Presence of clinical symptoms was associated with a longer evolution of the disease and a larger size of PU.1+ clones (p= 0.0003). Genetic lineage tracing of PU.1+ clones suggest a resident macrophage lineage or a bone marrow precursor origin depending on patients. Finally, a CSF1R-inhibitor depleted mutant microglia and limited neuronal loss in mice suggesting an alternative to MAPK inhibitors. These studies characterize a progressive neurodegenerative disease, caused by clonal proliferation of inflammatory microglia (CPIM), with a decade(s)-long preclinical stage of incipient disease that represent a therapeutic window for prevention of neuronal death.

CellMag-CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications

The recent push toward understanding an individual cell's behavior and identifying cellular heterogeneity has created an unmet need for technologies that can probe live cells at the single-cell level. Cells within a population are known to exhibit heterogeneous responses to environmental cues. These differences can lead to varied cellular states, behavior, and responses to therapeutics. Techniques are needed that are not only capable of processing and analyzing cellular populations at the single cell level, but also have the ability to isolate specific cell populations from a complex sample at high throughputs. The new CellMag-Coalesce-Attract-Resegment Wash (CellMag-CARWash) system combines positive magnetic selection with droplet microfluidic devices to isolate cells of interest from a mixture with >93% purity and incorporate treatments within individual droplets to observe single cell biological responses. This workflow is shown to be capable of probing the single cell extracellular vesicle (EV) secretion of MCF7 GFP cells. This article reports the first measurement of β-Estradiol's effect on EV secretion from MCF7 cells at the single cell level. Single cell processing revealed that MCF7 GFP cells possess a heterogeneous response to β-Estradiol stimulation with a 1.8-fold increase relative to the control.

Beyond single cells: microfluidics empowering multiomics analysis

Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.

Microbiome single cell atlases generated with a commercial instrument

Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes' genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters microbes by similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.

Chromosomal single-strand break repair and neurological disease: Implications on transcription and emerging genomic tools

Cells are constantly exposed to various sources of DNA damage that pose a threat to their genomic integrity. One of the most common types of DNA breaks are single-strand breaks (SSBs). Mutations in the repair proteins that are important for repairing SSBs have been reported in several neurological disorders. While several tools have been utilised to investigate SSBs in cells, it was only through recent advances in genomics that we are now beginning to understand the architecture of the non-random distribution of SSBs and their impact on key cellular processes such as transcription and epigenetic remodelling. Here, we discuss our current understanding of the genome-wide distribution of SSBs, their link to neurological disorders and summarise recent technologies to investigate SSBs at the genomic level.

Single-cell Multiomics Analysis of Myelodysplastic Syndromes and Clinical Response to Hypomethylating Therapy

Alterations in epigenetic marks, such as DNA methylation, represent a hallmark of cancer that has been successfully exploited for therapy in myeloid malignancies. Hypomethylating agents (HMA), such as azacitidine, have become standard-of-care therapy to treat myelodysplastic syndromes (MDS), myeloid neoplasms that can evolve into acute myeloid leukemia. However, our capacity to identify who will respond to HMAs, and the duration of response, remains limited. To shed light on this question, we have leveraged the unprecedented analytic power of single-cell technologies to simultaneously map the genome and immunoproteome of MDS samples throughout clinical evolution. We were able to chart the architecture and evolution of molecular clones in precious paired bone marrow MDS samples at diagnosis and posttreatment to show that a combined imbalance of specific cell lineages with diverse mutational profiles is associated with the clinical response of patients with MDS to hypomethylating therapy.

SIGNIFICANCE

MDS are myeloid clonal hemopathies with a low 5-year survival rate, and approximately half of the cases do not respond to standard HMA therapy. Our innovative single-cell multiomics approach offers valuable biological insights and potential biomarkers associated with the demethylating agent efficacy. It also identifies vulnerabilities that can be targeted using personalized combinations of small drugs and antibodies.

Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Motivation

Recently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is cost and time prohibitive. Multiplexing, in which cells from unique patients are pooled into a single experiment, offers a possible solution. While multiplexing methods exist for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need.

Results

Here, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Using SNACS, accuracy ranged from 0.948 - 0.991 vs 0.552 - 0.934 using demultiplexing methods from the single-cell literature.

Single-Cell DNA Sequencing and Immunophenotypic Profiling to Track Clonal Evolution in an Acute Myeloid Leukemia Patient

Single-cell DNA sequencing can address the sequence of somatic genetic events during myeloid transformation in relapsed acute myeloid leukemia (AML). We present an NPM1-mutated AML patient with an initial low ratio of FLT3-ITD (low-risk ELN-2017), treated with midostaurin combined with standard chemotherapy as front-line treatment, and with salvage therapy plus gilteritinib following allogenic stem cell transplantation after relapse. Simultaneous single-cell DNA sequencing and cell-surface immunophenotyping was used in diagnostic and relapse samples to understand the clinical scenario of this patient and to reconstruct the clonal composition of both tumors. Four independent clones were present before treatment: DNMT3A/DNMT3A/NPM1 (63.9%), DNMT3A/DNMT3A (13.9%), DNMT3A/DNMT3A/NPM1/FLT3 (13.8%), as well as a wild-type clone (8.3%), but only the minor clone with FLT3-ITD survived and expanded after therapy, being the most represented one (58.6%) at relapse. FLT3-ITD was subclonal and was found only in the myeloid blast population (CD38/CD117/CD123). Our study shows the usefulness of this approach to reveal the clonal architecture of the leukemia and the identification of small subclones at diagnosis and relapse that may explain how the neoplastic cells can escape from the activity of different treatments in a stepwise process that impedes the disease cure despite different stages of complete remission.

Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges

Atherosclerotic disease is a major cause of acute cardiovascular events. A deeper understanding of its underlying mechanisms will allow advancing personalized and patient-centered healthcare. Transcriptomic research has proven to be a powerful tool for unravelling the complex molecular pathways that drive atherosclerosis. However, low reproducibility of research findings and lack of standardization of procedures pose significant challenges in this field. In this review, we discuss how transcriptomic research can help in understanding the different phenotypes of the atherosclerotic plaque that contribute to the development and progression of atherosclerosis. We highlight the methodological challenges that need to be addressed to improve research outputs, and emphasize the importance of research protocols harmonization. We also discuss recent advances in transcriptomic research, including bulk or single-cell sequencing, and their added value in plaque phenotyping. Finally, we explore how integrated multiomics data and machine learning improve understanding of atherosclerosis and provide directions for future research.

ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.

Microfluidics-free single-cell genomics with templated emulsification

Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.

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.

Acute myeloid leukemia: from NGS, through scRNA-seq, to CAR-T. dissect cancer heterogeneity and tailor the treatment

Acute myeloid leukemia (AML) is a malignant blood cancer with marked cellular heterogeneity due to altered maturation and differentiation of myeloid blasts, the possible causes of which are transcriptional or epigenetic alterations, impaired apoptosis, and excessive cell proliferation. This neoplasm has a high rate of resistance to anticancer therapies and thus a high risk of relapse and mortality because of both the biological diversity of the patient and intratumoral heterogeneity due to the acquisition of new somatic changes. For more than 40 years, the old gold standard "one size fits all" treatment approach included intensive chemotherapy treatment with anthracyclines and cytarabine.The manuscript first traces the evolution of the understanding of the pathology from the 1970s to the present. The enormous strides made in its categorization prove to be crucial for risk stratification, enabling an increasingly personalized diagnosis and treatment approach.Subsequently, we highlight how, over the past 15 years, technological advances enabling single cell RNA sequencing and T-cell modification based on the genomic tools are affecting the classification and treatment of AML. At the dawn of the new millennium, the advent of high-throughput next-generation sequencing technologies has enabled the profiling of patients evidencing different facets of the same disease, stratifying risk, and identifying new possible therapeutic targets that have subsequently been validated. Currently, the possibility of investigating tumor heterogeneity at the single cell level, profiling the tumor at the time of diagnosis or after treatments exist. This would allow the identification of underrepresented cellular subclones or clones resistant to therapeutic approaches and thus responsible for post-treatment relapse that would otherwise be difficult to detect with bulk investigations on the tumor biopsy. Single-cell investigation will then allow even greater personalization of therapy to the genetic and transcriptional profile of the tumor, saving valuable time and dangerous side effects. The era of personalized medicine will take a huge step forward through the disclosure of each individual piece of the complex puzzle that is cancer pathology, to implement a "tailored" therapeutic approach based also on engineered CAR-T cells.

optima: an open-source R package for the Tapestri platform for integrative single cell multiomics data analysis

SUMMARY

The Tapestri platform offers DNA and protein analysis at the single-cell level. Integrating both types of data is beneficial for studying multiple cell populations in heterogeneous microenvironments, such as tumor tissues. Here, we present optima, an R package for the processing and analysis of data generated from the Tapestri platform. This package provides streamlined functionality for raw data filtering, integration, normalization, transformation, and visualization. Insights gained from the optima package help users to identify unique cell populations and uncover surface protein expression patterns. The results generated by optima help researchers elucidate dynamic changes at the single-cell level in heterogeneous microenvironments.

AVAILABILITY AND IMPLEMENTATION

This package is available in Github: https://github.com/rachelgriffard/optima.

Microbiome single cell atlases generated with a commercial instrument

Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes' genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters microbes by similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.

Clonal architecture and evolutionary history of Waldenström's macroglobulinemia at the single-cell level

To provide insight into the subclonal architecture and co-dependency patterns of the alterations in Waldenström's macroglobulinemia (WM), we performed single-cell mutational and protein profiling of eight patients. A custom panel was designed to screen for mutations and copy number alterations at the single-cell level in samples taken from patients at diagnosis (n=5) or at disease progression (n=3). Results showed that in asymptomatic WM at diagnosis, MYD88L265P was the predominant clonal alteration; other events, if present, were secondary and subclonal to MYD88L265P. In symptomatic WM, clonal diversity was more evident, uncovering combinations of alterations that synergized to promote clonal expansion and dominance. At disease progression, a dominant clone was observed, sometimes accompanied by other less complex minor clones, which could be consistent with a clonal selection process. Clonal diversity was also reduced, probably due to the effect of treatment. Finally, we combined protein expression with mutational analysis to map somatic genotype with the immunophenotype. Our findings provide a comprehensive view of the clonality of tumor populations in WM and how clonal complexity can evolve and impact disease progression.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

Multiomic Single Cell Sequencing Identifies Stemlike Nature of Mixed Phenotype Acute Leukemia and Provides Novel Risk Stratification

Mixed phenotype acute leukemia (MPAL) is a leukemia whose biologic drivers are poorly understood, therapeutic strategy remains unclear, and prognosis is poor. We performed multiomic single cell (SC) profiling of 14 newly diagnosed adult MPAL patients to characterize the immunophenotypic, genetic, and transcriptional landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. However, progressive acquisition of mutations is associated with increased expression of immunophenotypic markers of immaturity. Using SC transcriptional profiling, we find that MPAL blasts express a stem cell-like transcriptional profile distinct from other acute leukemias and indicative of high differentiation potential. Further, patients with the highest differentiation potential demonstrated inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in this cohort, is applicable to bulk RNA sequencing data and was predictive of survival in an independent patient cohort, suggesting utility for clinical risk stratification.

Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma

Despite improvements in cancer patient outcomes seen in the past decade, tumor resistance to therapy remains a major impediment to achieving durable clinical responses. Intratumoral heterogeneity related to genetic, epigenetic, transcriptomic, proteomic, and metabolic differences between individual cancer cells has emerged as a driver of therapeutic resistance. This cell to cell heterogeneity can be assessed using single cell profiling technologies that enable the identification of tumor cell clones that exhibit similar defining features like specific mutations or patterns of DNA methylation. Single cell profiling of tumors before and after treatment can generate new insights into the cancer cell characteristics that confer therapeutic resistance by identifying intrinsically resistant sub-populations that survive treatment and by describing new cellular features that emerge post-treatment due to tumor cell evolution. Integrative, single cell analytical approaches have already proven advantageous in studies characterizing treatment-resistant clones in cancers where pre- and post-treatment patient samples are readily available, such as leukemia. In contrast, little is known about other cancer subtypes like pediatric high grade glioma, a class of heterogeneous, malignant brain tumors in children that rapidly develop resistance to multiple therapeutic modalities, including chemotherapy, immunotherapy, and radiation. Leveraging single cell multi-omic technologies to analyze naïve and therapy-resistant glioma may lead to the discovery of novel strategies to overcome treatment resistance in brain tumors with dismal clinical outcomes. In this review, we explore the potential for single cell multi-omic analyses to reveal mechanisms of glioma resistance to therapy and discuss opportunities to apply these approaches to improve long-term therapeutic response in pediatric high grade glioma and other brain tumors with limited treatment options.

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.

Single-Cell Next-Generation Sequencing to Monitor Hematopoietic Stem-Cell Transplantation: Current Applications and Future Perspectives

Acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) are genetically complex and diverse diseases. Such complexity makes challenging the monitoring of response to treatment. Measurable residual disease (MRD) assessment is a powerful tool for monitoring response and guiding therapeutic interventions. This is accomplished through targeted next-generation sequencing (NGS), as well as polymerase chain reaction and multiparameter flow cytometry, to detect genomic aberrations at a previously challenging leukemic cell concentration. A major shortcoming of NGS techniques is the inability to discriminate nonleukemic clonal hematopoiesis. In addition, risk assessment and prognostication become more complicated after hematopoietic stem-cell transplantation (HSCT) due to genotypic drift. To address this, newer sequencing techniques have been developed, leading to more prospective and randomized clinical trials aiming to demonstrate the prognostic utility of single-cell next-generation sequencing in predicting patient outcomes following HSCT. This review discusses the use of single-cell DNA genomics in MRD assessment for AML/MDS, with an emphasis on the HSCT time period, including the challenges with current technologies. We also touch on the potential benefits of single-cell RNA sequencing and analysis of accessible chromatin, which generate high-dimensional data at the cellular resolution for investigational purposes, but not currently used in the clinical setting.

Droplet-based forward genetic screening of astrocyte-microglia cross-talk

Cell-cell interactions in the central nervous system play important roles in neurologic diseases. However, little is known about the specific molecular pathways involved, and methods for their systematic identification are limited. Here, we developed a forward genetic screening platform that combines CRISPR-Cas9 perturbations, cell coculture in picoliter droplets, and microfluidic-based fluorescence-activated droplet sorting to identify mechanisms of cell-cell communication. We used SPEAC-seq (systematic perturbation of encapsulated associated cells followed by sequencing), in combination with in vivo genetic perturbations, to identify microglia-produced amphiregulin as a suppressor of disease-promoting astrocyte responses in multiple sclerosis preclinical models and clinical samples. Thus, SPEAC-seq enables the high-throughput systematic identification of cell-cell communication mechanisms.

Adult Low-Hypodiploid Acute Lymphoblastic Leukemia Emerges from Preleukemic TP53-Mutant Clonal Hematopoiesis

Low hypodiploidy defines a rare subtype of B-cell acute lymphoblastic leukemia (B-ALL) with a dismal outcome. To investigate the genomic basis of low-hypodiploid ALL (LH-ALL) in adults, we analyzed copy-number aberrations, loss of heterozygosity, mutations, and cytogenetics data in a prospective cohort of Philadelphia (Ph)-negative B-ALL patients (n = 591, ages 18-84 years), allowing us to identify 80 LH-ALL cases (14%). Genomic analysis was critical for evidencing low hypodiploidy in many cases missed by cytogenetics. The proportion of LH-ALL within Ph-negative B-ALL dramatically increased with age, from 3% in the youngest patients (under 40 years old) to 32% in the oldest (over 55 years old). Somatic TP53 biallelic inactivation was the hallmark of adult LH-ALL, present in virtually all cases (98%). Strikingly, we detected TP53 mutations in posttreatment remission samples in 34% of patients. Single-cell proteogenomics of diagnosis and remission bone marrow samples evidenced a preleukemic, multilineage, TP53-mutant clone, reminiscent of age-related clonal hematopoiesis.

SIGNIFICANCE

We show that low-hypodiploid ALL is a frequent entity within B-ALL in older adults, relying on somatic TP53 biallelic alteration. Our study unveils a link between aging and low-hypodiploid ALL, with TP53-mutant clonal hematopoiesis representing a preleukemic reservoir that can give rise to aneuploidy and B-ALL. See related commentary by Saiki and Ogawa, p. 102. This article is highlighted in the In This Issue feature, p. 101.

Single-cell proteomics enabled by next-generation sequencing or mass spectrometry

In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.

Anterior gradient-2 regulates cell communication by coordinating cytokine-chemokine signaling and immune infiltration in breast cancer

Anterior gradient-2 (AGR2) is crucial to breast cancer progression. However, its role in the tumor immune microenvironment remains unclear. RNA sequencing expression profiles and associated clinical information were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. The AGR2 expression patterns were verified using clinical samples of breast cancer. Based on single-cell transcriptomic data, AGR2 expression patterns were identified and cell communication analysis was carried out. Furthermore, the roles of AGR2 in breast tumor progression were explored by a series of functional experiments. We found that DNA methylation was an important mechanism for regulating the expression patterns of AGR2. Patients with AGR2 low expression displayed an immune "hot" and immunosuppressive phenotype characterized by high abundance of tumor immune cell infiltration and increased enrichment scores for transforming growth factor-β (TGF-β) and epithelial-mesenchymal transition pathways, whereas patients with AGR2 high expression showed an opposite immunologic feature with a lack of immune cell infiltration, suggestive of an immune "cold" and desert phenotype. Moreover, single-cell analysis further revealed that AGR2 in malignant cells alters cell-cell interactions by coordinating cytokine-chemokine signaling and immune infiltration. Notably, two immunotherapy cohorts revealed that AGR2-coexpressed genes could serve as prognostic indicators of patient survival. In conclusion, AGR2 could promote breast cancer progression by affecting the tumor immune microenvironment. Patients with AGR2 low expression could be suitable for combination treatment with immune checkpoint inhibitor agents and TGF-β blockers. Therefore, this study provides a theoretical foundation for developing a strategy for personalized immunotherapy to patients with breast cancer.

Application of high-throughput single-nucleus DNA sequencing in pancreatic cancer

Despite insights gained by bulk DNA sequencing of cancer it remains challenging to resolve the admixture of normal and tumor cells, and/or of distinct tumor subclones; high-throughput single-cell DNA sequencing circumvents these and brings cancer genomic studies to higher resolution. However, its application has been limited to liquid tumors or a small batch of solid tumors, mainly because of the lack of a scalable workflow to process solid tumor samples. Here we optimize a highly automated nuclei extraction workflow that achieves fast and reliable targeted single-nucleus DNA library preparation of 38 samples from 16 pancreatic ductal adenocarcinoma patients, with an average library yield per sample of 2867 single nuclei. We demonstrate that this workflow not only performs well using low cellularity or low tumor purity samples but reveals genomic evolution patterns of pancreatic ductal adenocarcinoma as well.

Phenotypic signatures of immune selection in HIV-1 reservoir cells

Human immunodeficiency virus 1 (HIV-1) reservoir cells persist lifelong despite antiretroviral treatment1,2 but may be vulnerable to host immune responses that could be exploited in strategies to cure HIV-1. Here we used a single-cell, next-generation sequencing approach for the direct ex vivo phenotypic profiling of individual HIV-1-infected memory CD4+ T cells from peripheral blood and lymph nodes of people living with HIV-1 and receiving antiretroviral treatment for approximately 10 years. We demonstrate that in peripheral blood, cells harbouring genome-intact proviruses and large clones of virally infected cells frequently express ensemble signatures of surface markers conferring increased resistance to immune-mediated killing by cytotoxic T and natural killer cells, paired with elevated levels of expression of immune checkpoint markers likely to limit proviral gene transcription; this phenotypic profile might reduce HIV-1 reservoir cell exposure to and killing by cellular host immune responses. Viral reservoir cells harbouring intact HIV-1 from lymph nodes exhibited a phenotypic signature primarily characterized by upregulation of surface markers promoting cell survival, including CD44, CD28, CD127 and the IL-21 receptor. Together, these results suggest compartmentalized phenotypic signatures of immune selection in HIV-1 reservoir cells, implying that only small subsets of infected cells with optimal adaptation to their anatomical immune microenvironment are able to survive during long-term antiretroviral treatment. The identification of phenotypic markers distinguishing viral reservoir cells may inform future approaches for strategies to cure and eradicate HIV-1.

Molecular Characterization of Acquired Resistance to KRASG12C-EGFR Inhibition in Colorectal Cancer

With the combination of KRASG12C and EGFR inhibitors, KRAS is becoming a druggable target in colorectal cancer. However, secondary resistance limits its efficacy. Using cell lines, patient-derived xenografts, and patient samples, we detected a heterogeneous pattern of putative resistance alterations expected primarily to prevent inhibition of ERK signaling by drugs at progression. Serial analysis of patient blood samples on treatment demonstrates that most of these alterations are detected at a low frequency except for KRASG12C amplification, a recurrent resistance mechanism that rises in step with clinical progression. Upon drug withdrawal, resistant cells with KRASG12C amplification undergo oncogene-induced senescence, and progressing patients experience a rapid fall in levels of this alteration in circulating DNA. In this new state, drug resumption is ineffective as mTOR signaling is elevated. However, our work exposes a potential therapeutic vulnerability, whereby therapies that target the senescence response may overcome acquired resistance.

SIGNIFICANCE

Clinical resistance to KRASG12C-EGFR inhibition primarily prevents suppression of ERK signaling. Most resistance mechanisms are subclonal, whereas KRASG12C amplification rises over time to drive a higher portion of resistance. This recurrent resistance mechanism leads to oncogene-induced senescence upon drug withdrawal and creates a potential vulnerability to senolytic approaches. This article is highlighted in the In This Issue feature, p. 1.

ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model

Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent targeted scDNA-seq technologies, such as Mission Bio Tapestri, measure SNVs with high fidelity in individual cells, but yield much less reliable measurements of CNAs. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers and partial information about CNAs in the form of clustering of cells with similar copy-number profiles. This copy-number clustering constrains where loss of SNVs can occur in the phylogeny. We develop ConDoR (Constrained Dollo Reconstruction), an algorithm to infer tumor phylogenies from targeted scDNA-seq data using the constrained k-Dollo model. We show that ConDoR outperforms existing methods on simulated data. We use ConDoR to analyze a new multi-region targeted scDNA-seq dataset of 2153 cells from a pancreatic ductal adenocarcinoma (PDAC) tumor and produce a more plausible phylogeny compared to existing methods that conforms to histological results for the tumor from a previous study. We also analyze a metastatic colorectal cancer dataset, deriving a more parsimonious phylogeny than previously published analyses and with a simpler monoclonal origin of metastasis compared to the original study.

Code availability

Software is available at https://github.com/raphael-group/constrained-Dollo.

The present and future of measurable residual disease testing in acute myeloid leukemia

Considerable progress has been made in the past several years in the scientific understanding of, and available treatments for, acute myeloid leukemia (AML). Achievement of a conventional remission, evaluated cytomorphologically via small bone marrow samples, is a necessary but not sufficient step toward cure. It is increasingly appreciated that molecular or immunophenotypic methods to identify and quantify measurable residual disease (MRD) - populations of leukemia cells below the cytomorphological detection limit - provide refined information on the quality of response to treatment and prediction of the risk of AML recurrence and leukemia-related deaths. The principles and practices surrounding MRD remain incompletely determined however and the genetic and immunophenotypic heterogeneity of AML may prevent a one-sizefits- all approach. Here, we review the current approaches to MRD testing in AML, discuss strengths and limitations, highlight recent technological advances that may improve such testing, and summarize ongoing initiatives to generate the clinical evidence needed to advance the use of MRD testing in patients with AML.

Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows

The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.

Proteomics approaches to characterize the immune responses in cancer

Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.

Distinct Patterns of Clonal Evolution Drive Myelodysplastic Syndrome Progression to Secondary Acute Myeloid Leukemia

Clonal evolution in myelodysplastic syndrome (MDS) can result in clinical progression and secondary acute myeloid leukemia (sAML). To dissect changes in clonal architecture associated with this progression, we performed single-cell genotyping of paired MDS and sAML samples from 18 patients. Analysis of single-cell genotypes revealed patient-specific clonal evolution and enabled the assessment of single-cell mutational cooccurrence. We discovered that changes in clonal architecture proceed via distinct patterns, classified as static or dynamic, with dynamic clonal architectures having a more proliferative phenotype by blast count fold change. Proteogenomic analysis of a subset of patients confirmed that pathogenic mutations were primarily confined to primitive and mature myeloid cells, though we also identify rare but present mutations in lymphocyte subsets. Single-cell transcriptomic analysis of paired sample sets further identified gene sets and signaling pathways involved in two cases of progression. Together, these data define serial changes in the MDS clonal landscape with clinical and therapeutic implications.

SIGNIFICANCE

Precise clonal trajectories in MDS progression are made possible by single-cell genomic sequencing. Here we use this technology to uncover the patterns of clonal architecture and clonal evolution that drive the transformation to secondary AML. We further define the phenotypic and transcriptional changes of disease progression at the single-cell level. See related article by Menssen et al., p. 330 (31). See related commentary by Romine and van Galen, p. 270. This article is highlighted in the In This Issue feature, p. 265.

Single-Cell Sequencing: Ariadne’s Thread in the Maze of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a haematological neoplasm resulting from the accumulation of genetic and epigenetic alterations. Patients’ prognoses vary with AML genetic heterogeneity, which hampers successful treatments. Single-cell approaches have provided new insights of the clonal architecture of AML, revealing the mutational history from diagnosis, during treatment and to relapse. In this review, we imagine single-cell technologies as the Ariadne’s thread that will guide us out of the AML maze, provide a precise identikit of the leukemic cell at single-cell resolution and explore genomic, transcriptomic, epigenetic and proteomic levels.

Single cell proteogenomic sequencing identifies a relapse-fated AML subclone carrying FLT3-ITD with CN-LOH at chr13q

Internal tandem duplication of the Feline McDonough Sarcoma (FMS)-like tyrosine kinase 3 (FLT3-ITD) is one of the most clinically relevant mutations in acute myeloid leukemia (AML), with a high FLT3-ITD allelic ratio (AR) (≥0.5) being strongly associated with poor prognosis. FLT3-ITDs are heterogeneous, varying in size and location, with some patients having multiple FLT3-ITDs. Bulk cell-based approaches are limited in their ability to reveal the clonal structure in such cases. Using single-cell proteogenomic sequencing (ScPGseq), we attempted to identify a relapse-fated subclone in an AML case with mutations in WT1, NPM1, and FLT3 tyrosine kinase domain and two FLT3-ITDs (21 bp and 39 bp) (low AR) at presentation, then relapsed only with WT1 and NPM1 mutations and one FLT3-ITD (high AR). This relapse-fated subclone at presentation (∼2.1% of sequenced cells) was characterized by the presence of a homozygous 21 bp FLT3-ITD resulting from copy neutral loss of heterozygosity (CN-LOH) of chr13q and an aberrant, immature myeloid cell surface signature, contrast to the cell surface phenotype at presentation. In contrast to results from multicolor flow-cytometry, ScPGseq not only enabled the early detection of rare relapse-fated subclone showing immature myeloid signature but also highlighted the presence of homozygous 21 bp FLT3-ITDs in the clone at presentation.

Dual-layered hydrogels allow complete genome recovery with nucleic acid cytometry

Targeting specific cells for sequencing is important for applications in cancer, microbiology, and infectious disease. Nucleic acid cytometry is a powerful approach for accomplishing this because it allows specific cells to be isolated based on sequence biomarkers that are otherwise impossible to detect. However, existing methods require specialized microfluidic devices, limiting adoption. Here, we describe a modified workflow that uses particle-templated emulsification and flow cytometry to conduct the essential steps of cell detection and sorting normally accomplished by microfluidics. Our microfluidic-free workflow allows facile isolation and sequencing of cells, viruses, and nucleic acids and thus provides a powerful enrichment approach for targeted sequencing applications. This article is protected by copyright. All rights reserved.

Single-cell profiling of tumour evolution in multiple myeloma - opportunities for precision medicine

Multiple myeloma (MM) is a haematological malignancy of plasma cells characterized by substantial intraclonal genetic heterogeneity. Although therapeutic advances made in the past few years have led to improved outcomes and longer survival, MM remains largely incurable. Over the past decade, genomic analyses of patient samples have demonstrated that MM is not a single disease but rather a spectrum of haematological entities that all share similar clinical symptoms. Moreover, analyses of samples from monoclonal gammopathy of undetermined significance and smouldering MM have also shown the existence of genetic heterogeneity in precursor stages, in some cases remarkably similar to that of MM. This heterogeneity highlights the need for a greater dissection of underlying disease biology, especially the clonal diversity and molecular events underpinning MM at each stage to enable the stratification of individuals with a high risk of progression. Emerging single-cell sequencing technologies present a superlative solution to delineate the complexity of monoclonal gammopathy of undetermined significance, smouldering MM and MM. In this Review, we discuss how genomics has revealed novel insights into clonal evolution patterns of MM and provide examples from single-cell studies that are beginning to unravel the mutational and phenotypic characteristics of individual cells within the bone marrow tumour, immune microenvironment and peripheral blood. We also address future perspectives on clinical application, proposing that multi-omics single-cell profiling can guide early patient diagnosis, risk stratification and treatment strategies.

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

Blood malignancies provide unique opportunities for longitudinal tracking of disease evolution following therapeutic bottlenecks and for the monitoring of changes in anti-tumor immunity. The expanding development of multi-modal single-cell sequencing technologies affords newer platforms to elucidate the mechanisms underlying these processes at unprecedented resolution. Furthermore, the identification of molecular events that can serve as in-vivo barcodes now facilitate the tracking of the trajectories of malignant and of immune cell populations over time within primary human samples, as these permit unambiguous identification of the clonal lineage of cell populations within heterogeneous phenotypes. Here, we provide an overview of the potential for chromosomal copy number changes, somatic nuclear and mitochondrial DNA mutations, single nucleotide polymorphisms, and T and B cell receptor sequences to serve as personal natural barcodes and review technical implementations in single-cell analysis workflows. Applications of these methodologies include the study of acquired therapeutic resistance and the dissection of donor- and host cellular interactions in the context of allogeneic hematopoietic stem cell transplantation.

Understanding CLL biology through mouse models of human genetics

Rapid advances in large-scale next-generation sequencing studies of human samples have progressively defined the highly heterogeneous genetic landscape of chronic lymphocytic leukemia (CLL). At the same time, the numerous challenges posed by the difficulties in rapid manipulation of primary B cells and the paucity of CLL cell lines have limited the ability to interrogate the function of the discovered putative disease "drivers," defined in human sequencing studies through statistical inference. Mouse models represent a powerful tool to study mechanisms of normal and malignant B-cell biology and for preclinical testing of novel therapeutics. Advances in genetic engineering technologies, including the introduction of conditional knockin/knockout strategies, have opened new opportunities to model genetic lesions in a B-cell-restricted context. These new studies build on the experience of generating the MDR mice, the first example of a genetically faithful CLL model, which recapitulates the most common genomic aberration of human CLL: del(13q). In this review, we describe the application of mouse models to the studies of CLL pathogenesis and disease transformation from an indolent to a high-grade malignancy (ie, Richter syndrome [RS]) and treatment, with a focus on newly developed genetically inspired mouse lines modeling recurrent CLL genetic events. We discuss how these novel mouse models, analyzed using new genomic technologies, allow the dissection of mechanisms of disease evolution and response to therapy with greater depth than previously possible and provide important insight into human CLL and RS pathogenesis and therapeutic vulnerabilities. These models thereby provide valuable platforms for functional genomic analyses and treatment studies.