A new lymphoid-primed progenitor marked by Dach1 downregulation identified with single cell multi-omics.

A classical view of blood cell development is that multipotent hematopoietic stem and progenitor cells (HSPCs) become lineage-restricted at defined stages. Lin-c-Kit+Sca-1+Flt3+ cells, termed lymphoid-primed multipotent progenitors (LMPPs), have lost megakaryocyte and erythroid potential but are heterogeneous in their fate. Here, through single-cell RNA sequencing, we identify the expression of Dach1 and associated genes in this fraction as being coexpressed with myeloid/stem genes but inversely correlated with lymphoid genes. Through generation of Dach1-GFP reporter mice, we identify a transcriptionally and functionally unique Dach1-GFP- subpopulation within LMPPs with lymphoid potential with low to negligible classic myeloid potential. We term these 'lymphoid-primed progenitors' (LPPs). These findings define an early definitive branch point of lymphoid development in hematopoiesis and a means for prospective isolation of LPPs.

Lineage tracing reveals B cell antibody class switching is stochastic, cell-autonomous, and tuneable.

To optimize immunity to pathogens, B lymphocytes generate plasma cells with functionally diverse antibody isotypes. By lineage tracing single cells within differentiating B cell clones, we identified the heritability of discrete fate controlling mechanisms to inform a general mathematical model of B cell fate regulation. Founder cells highly influenced clonal plasma-cell fate, whereas class switch recombination (CSR) was variegated within clones. In turn, these CSR patterns resulted from independent all-or-none expression of both activation-induced cytidine deaminase (AID) and IgH germline transcription (GLT), with the latter being randomly re-expressed after each cell division. A stochastic model premised on these molecular transition rules accurately predicted antibody switching outcomes under varied conditions in vitro and during an immune response in vivo. Thus, the generation of functionally diverse antibody types follows rules of autonomous cellular programming that can be adapted and modeled for the rational control of antibody classes for potential therapeutic benefit.

Clonal multi-omics reveals Bcor as a negative regulator of emergency dendritic cell development.

Despite advances in single-cell multi-omics, a single stem or progenitor cell can only be tested once. We developed clonal multi-omics, in which daughters of a clone act as surrogates of the founder, thereby allowing multiple independent assays per clone. With SIS-seq, clonal siblings in parallel "sister" assays are examined either for gene expression by RNA sequencing (RNA-seq) or for fate in culture. We identified, and then validated using CRISPR, genes that controlled fate bias for different dendritic cell (DC) subtypes. This included Bcor as a suppressor of plasmacytoid DC (pDC) and conventional DC type 2 (cDC2) numbers during Flt3 ligand-mediated emergency DC development. We then developed SIS-skew to examine development of wild-type and Bcor-deficient siblings of the same clone in parallel. We found Bcor restricted clonal expansion, especially for cDC2s, and suppressed clonal fate potential, especially for pDCs. Therefore, SIS-seq and SIS-skew can reveal the molecular and cellular mechanisms governing clonal fate.

Transcription Factor PU.1 Promotes Conventional Dendritic Cell Identity and Function via Induction of Transcriptional Regulator DC-SCRIPT.

Dendritic cells (DCs) are can be broadly divided into conventional (cDC) and plasmacytoid (pDC) subsets. Despite the importance of this lineage diversity, its genetic basis is not fully understood. We found that conditional ablation of the Ets-family transcription factor PU.1 in DC-restricted progenitors led to increased pDC production at the expense of cDCs. PU.1 controlled many of the cardinal functions of DCs, such as antigen presentation by cDCs and type I interferon production by pDCs. Conditional ablation of PU.1 de-repressed the pDC transcriptional signature in cDCs. The combination of genome-wide mapping of PU.1 binding and gene expression analysis revealed a key role for PU.1 in maintaining cDC identity through the induction of the transcriptional regulator DC-SCRIPT. PU.1 activated DC-SCRIPT expression, which in turn promoted cDC formation, particularly of cDC1s, and repressed pDC development. Thus, cDC identity is regulated by a transcriptional node requiring PU.1 and DC-SCRIPT.

Non-genetic determinants of malignant clonal fitness at single-cell resolution.

All cancers emerge after a period of clonal selection and subsequent clonal expansion. Although the evolutionary principles imparted by genetic intratumour heterogeneity are becoming increasingly clear1, little is known about the non-genetic mechanisms that contribute to intratumour heterogeneity and malignant clonal fitness2. Here, using single-cell profiling and lineage tracing (SPLINTR)-an expressed barcoding strategy-we trace isogenic clones in three clinically relevant mouse models of acute myeloid leukaemia. We find that malignant clonal dominance is a cell-intrinsic and heritable property that is facilitated by the repression of antigen presentation and increased expression of the secretory leukocyte peptidase inhibitor gene (Slpi), which we genetically validate as a regulator of acute myeloid leukaemia. Increased transcriptional heterogeneity is a feature that enables clonal fitness in diverse tissues and immune microenvironments and in the context of clonal competition between genetically distinct clones. Similar to haematopoietic stem cells3, leukaemia stem cells (LSCs) display heritable clone-intrinsic properties of high, and low clonal output that contribute to the overall tumour mass. We demonstrate that LSC clonal output dictates sensitivity to chemotherapy and, although high- and low-output clones adapt differently to therapeutic pressure, they coordinately emerge from minimal residual disease with increased expression of the LSC program. Together, these data provide fundamental insights into the non-genetic transcriptional processes that underpin malignant clonal fitness and may inform future therapeutic strategies.

Clonal selection parallels between normal and cancer tissues.

Clonal selection and drift drive both normal tissue and cancer development. However, the biological mechanisms and environmental conditions underpinning these processes remain to be elucidated. Clonal selection models are centered in Darwinian evolutionary theory, where some clones with the fittest features are selected and populate the tissue or tumor. We suggest that different subclasses of stem cells, each of which is responsible for a distinct feature of the selection process, share common features between normal and cancer conditions. While active stem cells populate the tissue, dormant cells account for tissue replenishment/regeneration in both normal and cancerous tissues. We also discuss potential mechanisms that drive clonal drift, their interactions with clonal selection, and their similarities during normal and cancer tissue development.

A risk-reward examination of sample multiplexing reagents for single cell RNA-Seq.

Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for understanding cellular heterogeneity and function. However the choice of sample multiplexing reagents can impact data quality and experimental outcomes. In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, across diverse sample types such as human peripheral blood mononuclear cells (PBMCs), mouse embryonic brain and patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We compared multiple demultiplexing algorithms which differed in performance depending on data quality. We find that minor improvements to laboratory workflows such as titration and rapid processing are critical to optimal performance. We also compared the performance of fixed scRNA-Seq kits and highlight the advantages of the Parse Biosciences kit for fragile samples. Highly multiplexed scRNA-Seq experiments require more sequencing resources, therefore we evaluated CRISPR-based destruction of non-informative genes to enhance sequencing value. Our comprehensive analysis provides insights into the selection of appropriate sample multiplexing reagents and protocols for scRNA-Seq experiments, facilitating more accurate and cost-effective studies.

Guidelines for mouse and human DC generation.

This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. This article provides protocols with top ticks and pitfalls for preparation and successful generation of mouse and human DC from different cellular sources, such as murine BM and HoxB8 cells, as well as human CD34+ cells from cord blood, BM, and peripheral blood or peripheral blood monocytes. We describe murine cDC1, cDC2, and pDC generation with Flt3L and the generation of BM-derived DC with GM-CSF. Protocols for human DC generation focus on CD34+ cell culture on OP9 cell layers for cDC1, cDC2, cDC3, and pDC subset generation and DC generation from peripheral blood monocytes (MoDC). Additional protocols include enrichment of murine DC subsets, CRISPR/Cas9 editing, and clinical grade human DC generation. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.

The histone acetyltransferase HBO1 promotes efficient tip cell sprouting during angiogenesis.

Blood vessel growth and remodelling are essential during embryonic development and disease pathogenesis. The diversity of endothelial cells (ECs) is transcriptionally evident and ECs undergo dynamic changes in gene expression during vessel growth and remodelling. Here, we investigated the role of the histone acetyltransferase HBO1 (KAT7), which is important for activating genes during development and for histone H3 lysine 14 acetylation (H3K14ac). Loss of HBO1 and H3K14ac impaired developmental sprouting angiogenesis and reduced pathological EC overgrowth in the retinal endothelium. Single-cell RNA sequencing of retinal ECs revealed an increased abundance of tip cells in Hbo1-deficient retinas, which led to EC overcrowding in the retinal sprouting front and prevented efficient tip cell migration. We found that H3K14ac was highly abundant in the endothelial genome in both intra- and intergenic regions, suggesting that HBO1 acts as a genome organiser that promotes efficient tip cell behaviour necessary for sprouting angiogenesis. This article has an associated 'The people behind the papers' interview.

Spatial omics and multiplexed imaging to explore cancer biology.

Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

Artificial intelligence takes center stage: exploring the capabilities and implications of ChatGPT and other AI-assisted technologies in scientific research and education.

The emergence of large language models (LLMs) and assisted artificial intelligence (AI) technologies have revolutionized the way in which we interact with technology. A recent symposium at the Walter and Eliza Hall Institute explored the current practical applications of LLMs in medical research and canvassed the emerging ethical, legal and social implications for the use of AI-assisted technologies in the sciences. This paper provides an overview of the symposium's key themes and discussions delivered by diverse speakers, including early career researchers, group leaders, educators and policy-makers highlighting the opportunities and challenges that lie ahead for scientific researchers and educators as we continue to explore the potential of this cutting-edge and emerging technology.

Lymphoid tissue and plasmacytoid dendritic cells and macrophages do not share a common macrophage-dendritic cell-restricted progenitor.

The relationship between dendritic cells (DCs) and macrophages is often debated. Here we ask whether steady-state, lymphoid-tissue-resident conventional DCs (cDCs), plasmacytoid DCs (pDCs), and macrophages share a common macrophage-DC-restricted precursor (MDP). Using new clonal culture assays combined with adoptive transfer, we found that MDP fractions isolated by previous strategies are dominated by precursors of macrophages and monocytes, include some multipotent precursors of other hematopoietic lineages, but contain few precursors of resident cDCs and pDCs and no detectable common precursors restricted to these DC types and macrophages. Overall we find no evidence for a common restricted MDP leading to both macrophages and FL-dependent, resident cDCs and pDCs.

Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments.

Single cell RNA-sequencing (scRNA-seq) technology has undergone rapid development in recent years, leading to an explosion in the number of tailored data analysis methods. However, the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically compare the performance of the many methods available. Here, we generated a realistic benchmark experiment that included single cells and admixtures of cells or RNA to create 'pseudo cells' from up to five distinct cancer cell lines. In total, 14 datasets were generated using both droplet and plate-based scRNA-seq protocols. We compared 3,913 combinations of data analysis methods for tasks ranging from normalization and imputation to clustering, trajectory analysis and data integration. Evaluation revealed pipelines suited to different types of data for different tasks. Our data and analysis provide a comprehensive framework for benchmarking most common scRNA-seq analysis steps.

scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.

Single-cell RNA sequencing (scRNA-seq) technology allows researchers to profile the transcriptomes of thousands of cells simultaneously. Protocols that incorporate both designed and random barcodes have greatly increased the throughput of scRNA-seq, but give rise to a more complex data structure. There is a need for new tools that can handle the various barcoding strategies used by different protocols and exploit this information for quality assessment at the sample-level and provide effective visualization of these results in preparation for higher-level analyses. To this end, we developed scPipe, an R/Bioconductor package that integrates barcode demultiplexing, read alignment, UMI-aware gene-level quantification and quality control of raw sequencing data generated by multiple protocols that include CEL-seq, MARS-seq, Chromium 10X, Drop-seq and Smart-seq. scPipe produces a count matrix that is essential for downstream analysis along with an HTML report that summarises data quality. These results can be used as input for downstream analyses including normalization, visualization and statistical testing. scPipe performs this processing in a few simple R commands, promoting reproducible analysis of single-cell data that is compatible with the emerging suite of open-source scRNA-seq analysis tools available in R/Bioconductor and beyond. The scPipe R package is available for download from https://www.bioconductor.org/packages/scPipe.

Diverse and heritable lineage imprinting of early haematopoietic progenitors.

Haematopoietic stem cells (HSCs) and their subsequent progenitors produce blood cells, but the precise nature and kinetics of this production is a contentious issue. In one model, lymphoid and myeloid production branch after the lymphoid-primed multipotent progenitor (LMPP), with both branches subsequently producing dendritic cells. However, this model is based mainly on in vitro clonal assays and population-based tracking in vivo, which could miss in vivo single-cell complexity. Here we avoid these issues by using a new quantitative version of 'cellular barcoding' to trace the in vivo fate of hundreds of LMPPs and HSCs at the single-cell level. These data demonstrate that LMPPs are highly heterogeneous in the cell types that they produce, separating into combinations of lymphoid-, myeloid- and dendritic-cell-biased producers. Conversely, although we observe a known lineage bias of some HSCs, most cellular output is derived from a small number of HSCs that each generates all cell types. Crucially, in vivo analysis of the output of sibling cells derived from single LMPPs shows that they often share a similar fate, suggesting that the fate of these progenitors was imprinted. Furthermore, as this imprinting is also observed for dendritic-cell-biased LMPPs, dendritic cells may be considered a distinct lineage on the basis of separate ancestry. These data suggest a 'graded commitment' model of haematopoiesis, in which heritable and diverse lineage imprinting occurs earlier than previously thought.

Steady-state and inflammatory dendritic-cell development.

The developmental pathways that lead to the production of antigen-presenting dendritic cells (DCs) are beginning to be understood. These are the last of the pathways of haematopoiesis to be mapped. The existence of many specialized subtypes of DC has complicated this endeavour, as has the need to distinguish the DCs formed in steady state from those produced during an inflammatory response. Here we review studies that lead to the concept that different types of DC develop through different branches of haematopoietic pathways that involve different immediate precursor cells. Furthermore, these studies show that many individual tissues generate their own DCs locally, from a reservoir of immediate DC precursors, rather than depending on a continuous flux of DCs from the bone marrow.

Toward defining a 'lineage'--The case for dendritic cells.

The immune system consists of a heterogeneous ensemble of cell types that immunologists have tried to classify and order for decades. This classification has relied on varying criteria, resulting in major debates in the immunology community. Discovered in the late 1970s [1], dendritic cells (DCs) are no exception, and their membership to a distinct immune lineage is still vividly debated [2-6]. Here, we review recent work on the origin of DCs and discuss the possible definition of a separate 'DC lineage'.

Reproducibility of Illumina platform deep sequencing errors allows accurate determination of DNA barcodes in cells.

BACKGROUND: Next generation sequencing (NGS) of amplified DNA is a powerful tool to describe genetic heterogeneity within cell populations that can both be used to investigate the clonal structure of cell populations and to perform genetic lineage tracing. For applications in which both abundant and rare sequences are biologically relevant, the relatively high error rate of NGS techniques complicates data analysis, as it is difficult to distinguish rare true sequences from spurious sequences that are generated by PCR or sequencing errors. This issue, for instance, applies to cellular barcoding strategies that aim to follow the amount and type of offspring of single cells, by supplying these with unique heritable DNA tags. RESULTS: Here, we use genetic barcoding data from the Illumina HiSeq platform to show that straightforward read threshold-based filtering of data is typically insufficient to filter out spurious barcodes. Importantly, we demonstrate that specific sequencing errors occur at an approximately constant rate across different samples that are sequenced in parallel. We exploit this observation by developing a novel approach to filter out spurious sequences. CONCLUSIONS: Application of our new method demonstrates its value in the identification of true sequences amongst spurious sequences in biological data sets.