Lineage Information Research Articles

Temporal recording of mammalian development and precancer

Temporal ordering of cellular events offers fundamental insights into biological phenomena. Although this is traditionally achieved through continuous direct observations1,2, an alternative solution leverages irreversible genetic changes, such as naturally occurring mutations, to create indelible marks that enables retrospective temporal ordering3–5. Using a multipurpose, single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo, with incorporation of cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during mouse embryonic development, unconventional developmental relationships between cell types and new epithelial progenitor states by their unique genetic histories. Analysis of mouse adenomas, coupled to multiomic and single-cell profiling of human precancers, with clonal analysis of 418 human polyps, demonstrated the occurrence of polyclonal initiation in 15–30% of colonic precancers, showing their origins from multiple normal founders. Our study presents a multimodal framework that lays the foundation for in vivo recording, integrating synthetic or natural indelible genetic changes with single-cell analyses, to explore the origins and timing of development and tumorigenesis in mammalian systems.

Mapping lineage-traced cells across time points with moslin

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.

Demonstrating the data integrity of routinely collected healthcare systems data for clinical trials (DEDICaTe): A proof-of-concept study.

Introduction/aims: Healthcare systems data (also known as real-world or routinely collected health data) could transform the conduct of clinical trials. Demonstrating integrity and provenance of these data is critical for clinical trials, to enable their use where appropriate and avoid duplication using scarce trial resources. Building on previous work, this proof-of-concept study used a data intelligence tool, the "Central Metastore," to provide metadata and lineage information of nationally held data. Methods: The feasibility of NHS England's Central Metastore to capture detailed records of the origins, processes, and methods that produce four datasets was assessed. These were England's Hospital Episode Statistics (Admitted Patient Care, Outpatients, Critical Care) and the Civil Registration of Deaths (England and Wales). The process comprised: information gathering; information ingestion using the tool; and auto-generation of lineage diagrams/content to show data integrity. A guidance document to standardise this process was developed. Results/Discussion: The tool can ingest, store and display data provenance in sufficient detail to support trust and transparency in using these datasets for trials. The slowest step was information gathering from multiple sources, so consistency in record-keeping is essential.

Molecular recording using DNA Typewriter.

Recording molecular information to genomic DNA is a powerful means of investigating topics ranging from multicellular development to cancer evolution. With molecular recording based on genome editing, events such as cell divisions and signaling pathway activity drive specific alterations in a cell's DNA, marking the genome with information about a cell's history that can be read out after the fact. Although genome editing has been used for molecular recording, capturing the temporal relationships among recorded events in mammalian cells remains challenging. The DNA Typewriter system overcomes this limitation by leveraging prime editing to facilitate sequential insertions to an engineered genomic region. DNA Typewriter includes three distinct components: DNA Tape as the 'substrate' to which edits accrue in an ordered manner, the prime editor enzyme, and prime editing guide RNAs, which program insertional edits to DNA Tape. In this protocol, we describe general design considerations for DNA Typewriter, step-by-step instructions on how to perform recording experiments by using DNA Typewriter in HEK293T cells, and example scripts for analyzing DNA Typewriter data ( https://doi.org/10.6084/m9.figshare.22728758 ). This protocol covers two main applications of DNA Typewriter: recording sequential transfection events with programmed barcode insertions by using prime editing and recording lineage information during the expansion of a single cell to many. Compared with other methods that are compatible with mammalian cells, DNA Typewriter enables the recording of temporal information with higher recording capacities and can be completed within 4-6 weeks with basic expertise in molecular cloning, mammalian cell culturing and DNA sequencing data analysis.

Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools

Cellular barcoding is a lineage-tracing methodology that couples heritable synthetic barcodes to high-throughput sequencing, enabling the accurate tracing of cell lineages across a range of biological contexts. Recent studies have extended these methods by incorporating lineage information into single-cell or spatial transcriptomics readouts. Leveraging the rich biological information within these datasets requires dedicated computational tools for dataset pre-processing and analysis. Here, we present BARtab, a portable and scalable Nextflow pipeline, and bartools, an open-source R package, designed to provide an integrated end-to-end cellular barcoding analysis toolkit. BARtab and bartools contain methods to simplify the extraction, quality control, analysis, and visualization of lineage barcodes from population-level, single-cell, and spatial transcriptomics experiments. We showcase the utility of our integrated BARtab and bartools workflow via the analysis of exemplar bulk, single-cell, and spatial transcriptomics experiments containing cellular barcoding information.

Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets

Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub (https://github.com/UPSUTER/GEMLI).

Differential adhesion during development establishes individual neural stem cell niches and shapes adult behaviour in Drosophila.

Neural stem cells (NSCs) reside in a defined cellular microenvironment, the niche, which supports the generation and integration of newborn neurons. The mechanisms building a sophisticated niche structure around NSCs and their functional relevance for neurogenesis are yet to be understood. In the Drosophila larval brain, the cortex glia (CG) encase individual NSC lineages in membranous chambers, organising the stem cell population and newborn neurons into a stereotypic structure. We first found that CG wrap around lineage-related cells regardless of their identity, showing that lineage information builds CG architecture. We then discovered that a mechanism of temporally controlled differential adhesion using conserved complexes supports the individual encasing of NSC lineages. An intralineage adhesion through homophilic Neuroglian interactions provides strong binding between cells of a same lineage, while a weaker interaction through Neurexin-IV and Wrapper exists between NSC lineages and CG. Loss of Neuroglian results in NSC lineages clumped together and in an altered CG network, while loss of Neurexin-IV/Wrapper generates larger yet defined CG chamber grouping several lineages together. Axonal projections of newborn neurons are also altered in these conditions. Further, we link the loss of these 2 adhesion complexes specifically during development to locomotor hyperactivity in the resulting adults. Altogether, our findings identify a belt of adhesions building a neurogenic niche at the scale of individual stem cell and provide the proof of concept that niche properties during development shape adult behaviour.

Conserved and divergent signals in 5' splice site sequences across fungi, metazoa and plants.

In eukaryotic organisms the ensemble of 5' splice site sequences reflects the balance between natural nucleotide variability and minimal molecular constraints necessary to ensure splicing fidelity. This compromise shapes the underlying statistical patterns in the composition of donor splice site sequences. The scope of this study was to mine conserved and divergent signals in the composition of 5' splice site sequences. Because 5' donor sequences are a major cue for proper recognition of splice sites, we reasoned that statistical regularities in their composition could reflect the biological functionality and evolutionary history associated with splicing mechanisms. Results: We considered a regularized maximum entropy modeling framework to mine for non-trivial two-site correlations in donor sequence datasets corresponding to 30 different eukaryotes. For each analyzed species, we identified minimal sets of two-site coupling patterns that were able to replicate, at a given regularization level, the observed one-site and two-site frequencies in donor sequences. By performing a systematic and comparative analysis of 5'splice sites we showed that lineage information could be traced from joint di-nucleotide probabilities. We were able to identify characteristic two-site coupling patterns for plants and animals, and propose that they may echo differences in splicing regulation previously reported between these groups.

The role ofAPOBEC3-induced mutations inthe differential evolution ofmonkeypox virus.

Recent studies show that newly sampled monkeypox virus (MPXV) genomes exhibit mutations consistent with Apolipoprotein B mRNA Editing Catalytic Polypeptide-like3 (APOBEC3)-mediated editing compared to MPXV genomes collected earlier. It is unclear whether these single-nucleotide polymorphisms (SNPs) result from APOBEC3-induced editing or are a consequence of genetic drift within one or more MPXV animal reservoirs. We develop a simple method based on a generalization of the General-Time-Reversible model to show that the observed SNPs are likely the result of APOBEC3-induced editing. The statistical features allow us to extract lineage information and estimate evolutionary events.

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 transcriptome atlas of Drosophila gastrula 2.0

During development, positional information directs cells to specific fates, leading them to differentiate with their own transcriptomes and express specific behaviors and functions. However, the mechanisms underlying these processes in a genome-wide view remain ambiguous, partly because the single-cell transcriptomic data of early developing embryos containing accurate spatial and lineage information are still lacking. Here, we report a single-cell transcriptome atlas of Drosophila gastrulae, divided into 77 transcriptomically distinct clusters. We find that the expression profiles of plasma-membrane-related genes, but not those of transcription-factor genes, represent each germ layer, supporting the nonequivalent contribution of each transcription-factor mRNA level to effector gene expression profiles at the transcriptome level. We also reconstruct the spatial expression patterns of all genes at the single-cell stripe level as the smallest unit. This atlas is an important resource for the genome-wide understanding of the mechanisms by which genes cooperatively orchestrate Drosophila gastrulation.

Single-cell Spatial Phenotyping of Immune and Metabolic Landscapes in Systemic Lupus Erythematosus

Abstract Autoimmune diseases like systemic lupus erythematosus (SLE) affect the health of multiple organ systems including the central nervous system, skin, joints, and kidneys. The heterogeneity of clinical profiles observed with SLE reflects a complex dysregulation of cellular processes, including metabolism, apoptotic clearance, and inflammation. Diagnosis and treatment of SLE is thus challenging, and few new treatments have been approved over the past 60 years. In this study, we aimed to characterize the spatial immune and metabolic profiles of SLE at single cell resolution. To do so, we profiled a cohort of human FFPE SLE tissues with an ultrahigh-plex panel of >50 oligo-conjugated antibodies representing immune, apoptotic, metabolic and stress markers. Whole-slide, spatial phenotyping was performed using the PhenoCycler ®-Fusion system, followed by deep bioinformatic analyses to determine the cellular phenotypes, spatial neighborhoods, and metabolic determinants of SLE. By combining immune cell lineage information with expression of key proteins involved in glycolysis, amino acid metabolism, lipid metabolism, hypoxia, oxidative stress, and apoptosis, we elucidated the functional dysregulation in key immune subsystems that may contribute to development of autoimmunity and inflammation associated with SLE. Spatial phenotyping also revealed a high degree of inter- and intra-sample heterogeneity, confirming the complexity of the SLE clinical profile. Collectively, our data amount to new insights into metabolic reprogramming of immune cells in autoimmune diseases and may aid in the identification of novel therapeutic biomarkers within the tissue microenvironment of SLE patients.

A Guide to Gene-Centric Analysis Using TreeSAPP.

Gene-centric analysis is commonly used to chart the structure, function, and activity of microbial communities in natural and engineered environments. A common approach is to create custom ad hoc reference marker gene sets, but these come with the typical disadvantages of inaccuracy and limited utility beyond assigning query sequences taxonomic labels. The Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software package standardizes analysis of phylogenetic and functional marker genes and improves predictive performance using a classification algorithm that leverages information-rich reference packages consisting of a multiple sequence alignment, a profile hidden Markov model, taxonomic lineage information, and a phylogenetic tree. Here, we provide a set of protocols that link the various analysis modules in TreeSAPP into a coherent process that both informs and directs the user experience. This workflow, initiated from a collection of candidate reference sequences, progresses through construction and refinement of a reference package to marker identification and normalized relative abundance calculations for homologous sequences in metagenomic and metatranscriptomic datasets. The alpha subunit of methyl-coenzyme M reductase (McrA) involved in biological methane cycling is presented as a use case given its dual role as a phylogenetic and functional marker gene driving an ecologically relevant process. These protocols fill several gaps in prior TreeSAPP documentation and provide best practices for reference package construction and refinement, including manual curation steps from trusted sources in support of reproducible gene-centric analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating reference packages Support Protocol 1: Installing TreeSAPP Support Protocol 2: Annotating traits within a phylogenetic context Basic Protocol 2: Updating reference packages Basic Protocol 3: Calculating relative abundance of genes in metagenomic and metatranscriptomic datasets.

Document Based Data Sharing Platform Architecture

The Big Data contains essential information for large organizations to provide new insight potential. Due to the new technological developments that have developed with Industry 4.0, data is produced in increasing volumes. Data Sharing Platforms are needed to cope with the volumes of this data and to transform data into valuable information. In line with this need, a document-based data-sharing platform software architecture is proposed within the scope of this research. The Data Sharing Platform Architecture we recommend; is designed for a document-based data management platform designed to process data at scale for analytical purposes. In the proposed study, Metadata management is used to prevent the large volume of data obtained from becoming complex and unusable. The proposed architecture has a metadata store with an enriched toolset to identify the data owner and store the version and lineage information. In the study, to provide easy access to the correct data, the locations of the data needed are shown to the users in detailed figures. To clean the data in the most appropriate quality, additional development studies are integrated into the system that will enable the user to pre-process the data. There is an operational security control to use the data securely. A standard user group management, which may vary according to operating systems, is integrated into the proposed software architecture. Again, the proposed software architecture categorizes the data by tagging it in stochastic data sets. It can offer suggestions in a way that can make suggestions according to the roles of the following users. In addition, a version and rule adaptation method is provided to deal with changes over time. A personalized rule customization method is proposed to meet the system's need to respond to the specific needs of each user.We present the details of the document-based data-sharing platform software architecture we are developing within the scope of this conference paper.

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity

Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to therapeutic stress. Such phenotypic plasticity may confer resistance, but also presents opportunities to identify molecular programs that could be targeted for therapeutic benefit. Approaches to quantify tumor-drug responses typically focus on snapshot, population-level measurements. While informative, these methods lack lineage and temporal information, which are particularly critical for understanding dynamic processes such as cell state switching. As new technologies have become available to measure lineage relationships, modeling approaches will be needed to identify the forms of cell-to-cell heterogeneity present in these data. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of phenotypic heterogeneity and state transitions. In benchmarking studies, we demonstrated that the model successfully classifies cells within experimentally-tractable dataset sizes. As an application, we analyzed experimental measurements in cancer and non-cancer cell populations under various treatments. We find evidence of multiple phenotypically distinct states, with considerable heterogeneity and unique drug responses. In total, this framework allows for the flexible modeling of single cell heterogeneity across lineages to quantify, understand, and control cell state switching.

Intratumor heterogeneity of driver mutations and TMB distribution in 30 early-stage LUAD patients with multiple lesions.

BackgroundDifferentiating multiple pulmonary lesions as multiple primary lung cancer (MLC) or intra-pulmonary metastasis (IPM) is critical. Lung cancer also has a high genetic heterogeneity, which influenced the treatment strategy. Genetic information may aid in tracing lineage information on multiple lung lesions. This study applied comprehensive genomic profiling to decipher the intrinsic genetics of multiple lung lesions.MethodsSixty-six lung adenocarcinomas (LUAD) tumor lesions (FFEP) archived from 30 patients were included in this study. The 508 cancer-related genes were evaluated by targeted next-generation sequencing (MGI-seq 2000).ResultsThe study included a total of 30 LUADs (66 samples). The majority of tumors demonstrated intra-tumoral heterogeneity. Two hundred twenty-four mutations were detected by sequencing the 66 samples. We investigated the driver gene mutations of NSCLC patients with multiple lesions. EGFR was the most frequently (48/198) mutated driver gene. The codons in EGFR mainly affected by mutations were p.L858R (18/66 [27.3%]) and exon 19del (8/66 [12.1%]). In addition, additional driver genes were found, including TP53, BRAF, ERBB2, MET, and PIK3CA. We also found that the inter-component heterogeneity of different lesions and more than two different mutation types of EGFR were detected in seven patients with two lesions (P3, P10, P24, P25, P28, P29, and P30). The TMB values of different lesions in each patient were different in 26 patients (except P4, P5, P14, and P30).ConclusionsComprehensive genomic profiling should be applied to distinguishing the nature of multiple lung lesions irrespective of radiologic and histologic diagnoses.

In preprints: the fast-paced field of single-cell lineage tracing

In preprints: the fast-paced field of single-cell lineage tracing

Genetic and genomic analysis of oxygen consumption in mice.

We estimated genetic parameters for oxygen consumption (OC), OC per metabolic body weight (OCMBW) and body weight at three through 8 weeks of age in divergently selected mice populations, with an animal model considering maternal genetic, common litter environmental and cytoplasmic inheritance effects. Cytoplasmic inheritance was considered based on maternal lineage information. With respect to OC, estimated direct heritability was moderate (0.32) and the estimated proportion of the variance of cytoplasmic inheritance effects to the phenotypic variance was very low (0.01), implying that causal genes for OC could be located on autosomes. To assess this hypothesis, we attempted to identify possible candidate causal genes through selective signature detection with the results of pooled whole-genome resequencing using pooled DNA samples from high and low OC mice. We made a list of possible candidate causal genes for OC, including those relating to electron transport chain and ATP-binding proteins (Ndufa12, Sdhc, Atp10b, etc.), Prr16 encoding Largen protein, Cry1 encoding a key component of the circadian core oscillator and so on. The results, although careful interpretation must be required, could contribute to elucidate the genetic mechanism of OC, an indicator for maintenance energy requirement, and therefore feed efficiency.

Abstract B028: Overcoming resistance by driving melanoma cells into drug-susceptible states

Abstract The goal of this study is to target pre-existing heterogeneity in gene expression to ultimately overcome resistance to targeted therapy in melanoma. Despite significant advances in the treatment of melanoma, many patients still fail to achieve durable responses to therapy due to the development of drug resistance. Our lab recently discovered that in drug-naive melanoma rare cells transiently enter distinct gene expression states that allows them to ultimately survive treatment with targeted therapy (we refer to these cells as “primed for drug resistance”). The goal of this work is to develop a strategy to overcome this form of resistance by driving cells out of states primed for resistance and into drug-susceptible states. To identify the pathways that regulate the transition to the primed state we combined single-cell RNA sequencing with DNA barcoding to extract the transcriptional profiles and lineage information of over 7,000 drug-naive melanoma cells. We found that cells in lineages that transitioned into states primed for drug resistance express high levels of genes associated with TGFβ and PI3K signaling compared to lineages that stayed in a drug-susceptible state. To test whether TGFβ can directly cause cells to enter the primed state, we treated cells with recombinant TGFβ which led to a significant increase in the number of primed cells. Intriguingly, we also found that treating with TGFB receptor inhibitor or PI3K inhibitor reduced the number of cells in the primed cell state. Further, we sequentially treated these cells first with either the TGFB or PI3K inhibitor followed by targeted therapy and ultimately reduced the number of resistant colonies, demonstrating the potential for this therapeutic paradigm. To better understand how targeting the TGFB and PI3K pathways shift the population dynamics, we used single-cell RNA-seq and cellular barcoding to track lineages following treatment with TGFB, TGFB inhibitor, and PI3K inhibitor. Our analysis reveals the population changes following each therapy and directly demonstrates that treating with PI3K inhibitor decreases the number of primed cells, shifting the population into a drug-susceptible expression state. Ultimately, this work defines two key pathways underlying the primed cell state in melanoma and presents a therapeutic paradigm to eliminate this form of therapy resistance. Citation Format: Guillaume Harmange, Ben Emert, Dylan Schaff, Raul Reyes-Hueros, Sydney Shaffer. Overcoming resistance by driving melanoma cells into drug-susceptible states [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B028.

Using in vivo barcoding to capture T versus B cell fate commitment in the thymus.

Abstract B cells are a rare subset in the thymus, an environment that supports T cell development and inhibits commitment to other immune cell lineages. Yet thymic B cells play an important role in the negative selection of autoreactive T cells. The origin of thymic B cells from either committed B cells or early thymocyte progenitors remains uncertain. We hypothesize that early thymic progenitor cells are a heterogenous population that can give rise to both T cells and B cells. In our study we combined the CRISPR/Cas9-dependent lineage recorder GESTALT with single cell RNAseq to capture cell lineage and type information to follow progressive cell fate decisions and to identify thymic B cell progenitors and transcriptional regulators permitting B cell differentiation in the thymus. Additionally, to evaluate the functional importance of thymic B cells in shaping the T cell repertoire, we performed TCR-seq on conventional CD4 T cells and Tregs. In the absence of B cells, amino acid usage within either TCR alpha or beta CDR3 regions remained unchanged. However, the dominant TCR alpha CDR3 length from the most abundant thymic Treg clones was reduced compared to wild type. Whether this repertoire shift impacts self-tolerance to B cell or AIRE-dependent antigens, both of which are expressed by thymic B cells, remains to be determined. Together, our data directly assesses lineage relationships leading to thymic T and B cell fate commitment and deepen our understanding of intra-thymic development and function of B cells that are required for central tolerance. Supported by AI089805 Burroughs-Wellcome Fellowship