Individual Cells Research Articles

Endothelial cell heterogeneity in colorectal cancer: tip cells drive angiogenesis

This study aims to uncover the heterogeneity of endothelial cells (ECs) in colorectal cancer (CRC) and their crucial role in angiogenesis, with a special focus on tip cells. Using single-cell RNA sequencing to profile ECs, our data suggests that CRC ECs predominantly exhibit enhanced angiogenesis and decreased antigen presentation, a shift in phenotype largely steered by tip cells. We also observed that an increase in the density and proportion of tip cells correlates with CRC occurrence, progression, and poorer patient prognosis. Furthermore, we identified endothelial cell-specific molecule 1 (ESM1), specifically expressed in tip cells, sustains a VEGFA-KDR-ESM1 positive feedback loop, promoting angiogenesis and CRC proliferation and migration. We also found the enrichment of KDR in tip cells and spotlight a unique long-tail effect in VEGFA expression: while VEGFA is primarily expressed by epithelial cells, the highest level of VEGFA expression is found in individual myeloid cells. Moreover, we observed that effective PD-1 blockade immunotherapy significantly reduced tip cells, disrupting the VEGFA-KDR-ESM1 positive feedback loop in the process. Our investigation into the heterogeneity of ECs in CRC at a single-cell level offers important insights that may contribute to the development of more effective immunotherapies targeting tip cells in CRC.

Effect of active loop extrusion on the two-contact correlations in the interphase chromosome.

The population-averaged contact maps generated by the chromosome conformation capture technique provide important information about the average frequency of contact between pairs of chromatin loci as a function of the genetic distance between them. However, these datasets do not tell us anything about the joint statistics of simultaneous contacts between genomic loci in individual cells. This kind of statistical information can be extracted using the single-cell Hi-C method, which is capable of detecting a large fraction of simultaneous contacts within a single cell, as well as through modern methods of fluorescent labeling and super-resolution imaging. Motivated by the prospect of the imminent availability of relevant experimental data, in this work, we theoretically model the joint statistics of pairs of contacts located along a line perpendicular to the main diagonal of the single-cell contact map. The analysis is performed within the framework of an ideal polymer model with quenched disorder of random loops, which, as previous studies have shown, allows us to take into account the influence of the loop extrusion process on the conformational properties of interphase chromatin.

Description of Bacterial RNA Transcripts Detected in Mycobacterium tuberculosis - Infected Cells from Peripheral Human Granulomas using Single Cell RNA Sequencing.

Mycobacterium tuberculosis (Mtb) remains a global human health threat and a significant cause of human morbidity and mortality. We document here the capture of Mtb transcripts in libraries designed to amplify eukaryotic mRNA. These reads are often considered spurious or nuisance and are rarely investigated. Because of early literature suggesting the possible presence of polyadenylated transcripts in Mtb RNA, we included the H37Rv Mtb reference genome when assembling scRNA seq libraries from fine needle aspirate samples from patients presenting at the TB clinic, Port Moresby General Hospital, Papua New Guinea. We used 10X Genomics single-cell RNA sequencing transcriptomics pipeline, which initiates mRNA amplification with poly-T primers on ~30-micron beads designed to capture, in this case, human mRNA associated with individual cells in the clinical samples. Utilizing the 10X Genomics Cell Ranger tool to align sequencing reads, we consistently detected bacterial small and large ribosomal subunit RNA sequences (rrs and rrl, respectively) and other bacterial gene transcripts in the cell culture and patient samples. We interpret Mtb reads associated with the host cell's unique molecular identifier (UMI) and transcriptome to indicate infection of that individual host cell. The Mtb transcripts detected showed frequent sequence variation from the reference genome, with greater than 90% of the rrs or rrl reads from many clinical samples having at least 1 sequence difference compared to the H37Rv reference genome. The data presented includes only bacterial sequences from patients with TB infections that were confirmed by the hospital pathology lab using acid-fast microscopy and/or GeneXpert analysis. The repeated, non-random nature of the sequence variations detected in Mtb rrs and rrl transcripts from multiple patients, suggests that, even though this appears to be a stochastic process, there is possibly some selective pressure that limits the types and locations of sequence variation allowed. The variation does not appear to be entirely artefactual, and it is hypothesized that it could represent an additional mechanism of adaptation to enhance bacterial fitness against host defenses or chemotherapy.

MassLite: An integrated python platform for single cell mass spectrometry metabolomics data pretreatment with graphical user interface and advanced peak alignment method

Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency.

Cell fate simulation reveals cancer cell features in the tumor microenvironment

To elucidate the dynamic evolution of cancer cell characteristics within the tumor microenvironment (TME), we developed an integrative approach combining single-cell tracking, cell fate simulation, and three-dimensional (3D) TME modeling. We began our investigation by analyzing the spatiotemporal behavior of individual cancer cells in cultured pancreatic (MiaPaCa2) and cervical (HeLa) cancer cell lines, with a focus on the α2-6 sialic acid (α2-6Sia) modification on glycans, which is associated with cell stemness. Our findings revealed that MiaPaCa2 cells exhibited significantly higher levels of α2-6Sia modification, correlating with enhanced reproductive capabilities, whereas HeLa cells showed less prevalence of this modification. To accommodate the in vivo variability of α2-6Sia levels, we employed a cell fate simulation algorithm that digitally generates cell populations based on our observed data while varying the level of sialylation, thereby simulating cell growth patterns. Subsequently, we performed a 3D TME simulation with these deduced cell populations, considering the microenvironment that could impact cancer cell growth. Immune cell landscape information derived from 193 cervical and 172 pancreatic cancer cases was used to estimate the degree of the positive or negative impact. Our analysis suggests that the deduced cells generated based on the characteristics of MiaPaCa2 cells are less influenced by the immune cell landscape within the TME compared to those of HeLa cells, highlighting that the fate of cancer cells is shaped by both the surrounding immune landscape and the intrinsic characteristics of the cancer cells.

Modeling the profibrotic microenvironment in vitro: Model validation

Establishing the molecular and cellular mechanisms of fibrosis requires the development of validated and reproducible models. The complexity of in vivo models challenges the monitoring of an individual cell fate, in some cases making it impossible. However, the set of factors affecting cells in vitro culture systems differ significantly from in vivo conditions, insufficiently reproducing living systems. Thus, to model profibrotic conditions in vitro, usually the key profibrotic factor, transforming growth factor beta (TGFβ-1) is used as a single factor. TGFβ-1 stimulates the differentiation of fibroblasts into myofibroblasts, the main effector cells promoting the development and progression of fibrosis. However, except for soluble factors, the rigidity and composition of the extracellular matrix (ECM) play a critical role in the differentiation process. To develop the model of more complex profibrotic microenvironment in vitro, we used a combination of factors: decellularized ECM synthesized by human dermal fibroblasts in the presence of ascorbic acid if cultured as cell sheets and recombinant TGFβ-1 as a supplement. When culturing human mesenchymal stromal cells derived from adipose tissue (MSCs) under described conditions, we observed differentiation of MSCs into myofibroblasts due to increased number of cells with stress fibrils with alpha-smooth muscle actin (αSMA), and increased expression of myofibroblast marker genes such as collagen I, EDA-fibronectin and αSMA. Importantly, secretome of MSCs changed in these profibrotic microenvironment: the secretion of the profibrotic proteins SPARC and fibulin-2 increased, while the secretion of the antifibrotic hepatocyte growth factor (HGF) decreased. Analysis of transciptomic pattern of regulatory microRNAs in MSCs revealed 49 miRNAs with increased expression and 3 miRNAs with decreased expression under profibrotic stimuli. Bioinformatics analysis confirmed that at least 184 gene targets of the differently expressed miRNAs genes were associated with fibrosis. To further validate the developed model of profibrotic microenvironment, we cultured human dermal fibroblasts in these conditions and observed increased expression of fibroblast activation protein (FAPa) after 12 h of cultivation as well as increased level of αSMA and higher number of αSMA + stress fibrils after 72 h. The data obtained allow us to conclude that the conditions formed by the combination of profibrotic ECM and TGFβ-1 provide a complex profibrotic microenvironment in vitro. Thus, this model can be applicable in studying the mechanism of fibrosis development, as well as for the development of antifibrotic therapy.

Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data.

Single-cell RNA sequencing (scRNA-seq) data are widely used to study cancer cell states and their heterogeneity. However, the tumour microenvironment is usually a mixture of healthy and cancerous cells and it can be difficult to fully separate these two populations based on transcriptomics alone. If available, somatic single-nucleotide variants (SNVs) observed in the scRNA-seq data could be used to identify the cancer population and match that information with the single cells' expression profile. However, calling somatic SNVs in scRNA-seq data is a challenging task, as most variants seen in the short-read data are not somatic, but can instead be germline variants, RNA edits or transcription, sequencing, or processing errors. In addition, only variants present in actively transcribed regions for each individual cell will be seen in the data. To address these challenges, we develop CCLONE (Cancer Cell Labelling On Noisy Expression), an interpretable tool adapted to handle the uncertainty and sparsity of SNVs called from scRNA-seq data. CCLONE jointly identifies cancer clonal populations, and their associated variants. We apply CCLONE on two acute myeloid leukaemia datasets and one lung adenocarcinoma dataset and show that CCLONE captures both genetic clones and somatic events for multiple patients. These results show how CCLONE can be used to gather insight into the course of the disease and the origin of cancer cells in scRNA-seq data. Source code is available at github.com/HaghverdiLab/CCLONE.

DIVERGENT PROPERTIES OF SKELETAL STEM CELLS FROM THE NECROTIC AND NON-NECROTIC ZONES OF FEMORAL HEADS IN OSTEONECROSIS OF THE FEMORAL HEAD

Bone marrow stem cells (BMSCs) represent a collection of different cell types exhibiting stem cell characteristics but with notable heterogeneity. Among these, Skeletal Stem Cells (SSCs) represent a distinct matrix subgroup within BMSC and demonstrate a specialized capacity to facilitate bone formation, recruit chondrocytes, and contribute to hematopoiesis. SSCs play a pivotal role in orchestrating the functions of skeletal organs. Local ischemia has a significant impact on cell survival and function. We hypothesize that bone ischemia induces alterations in the differentiation potential of SSCs, consequently influencing changes in bone structure.We mechanically dissected tissue from the necrotic segment in the femoral head and more normal appearing areas from the femoral neck of specimens from 5 patients diagnosed with osteonecrosis of the femoral head (ONFH). These tissues were enzymatically broken down into individual cell suspensions. Utilizing fluorescence-activated cell sorting (FACS) based on specific surface markers indicative of human skeletal stem cells (hSSC), namely CD45- CD235a- CD31- TIE2- Podoplanin (PDPN)+ CD146- CD73+ CD164+, we isolated a distinct cell population. Subsequent in vitro evaluations, focusing on clonogenicity, osteogenesis, and chondrogenesis were conducted to assess the functional prowess of these SSCs. Moreover, we introduced BMP2 at a concentration of 50ng/ml to SSCs extracted from necrotic regions to potentially reinstate their osteogenic capabilities.We effectively isolated SSCs from both Necrotic and Non-necrotic Zones. We observed an augmented clonal formation capacity and chondrogenesis ability of SSCs isolated from the necrotic region, accompanied by a significant decline in osteogenic ability (P＜0.01), an effect not reversible even with the addition of BMP2.Ischemia adversely affects the proliferation and function of SSCs, resulting in a diminished osteogenic capacity and an insensitivity to BMP2, ultimately leading to structural alterations in bone tissue.

From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single-Cell Sequencing.

Heart failure (HF) is a prevalent cardiovascular disease with significant morbidity and mortality rates worldwide. Due to the intricate structure of the heart, diverse cell types, and the complex pathogenesis of HF, further in-depth investigation into the underlying mechanismsis required. The elucidation of the heterogeneity of cardiomyocytes and the intercellular communication network is particularly important. Traditional high-throughput sequencing methods provide an average measure of gene expression, failing to capture the "heterogeneity" between cells and impacting the accuracy of gene function knowledge. In contrast, single-cell sequencing techniques allow for the amplification of the entire genome or transcriptome at the individual cell level, facilitating the examination of gene structure and expression with unparalleled precision. This approach offers valuable insights into disease mechanisms, enabling the identification of changes in cellular components and gene expressions during hypertrophy associated with HF. Moreover, it reveals distinct cell populations and their unique roles in the HF microenvironment, providing a comprehensive understanding of the cellular landscape that underpins HF pathogenesis. This review focuses on the insights provided by single-cell sequencing techniques into the mechanisms underlying HF and discusses the challenges encountered in current cardiovascular research.

Chaotic dynamics for homeostatic hematopoiesis.

Hematopoiesis is a highly dynamical and stochastic process, challenging our understanding of homeostasis. Clinical studies of leukemia or neutropenic patients revealed that multiple blood cell types fluctuate spontaneously with large yet regular oscillations of their frequencies. Yet the stability of hematopoiesis in healthy individuals remains understudied. Here we report on both cross-sectional and longitudinal studies of dozens of healthy mice, through high-dimensional mass and spectral cytometry, to understand hematopoiesis at homeostasis. We found that all cell types in the bone marrow, blood, and spleen exhibit large variations of frequency (e.g., with coefficients of variation larger than 1). While the frequencies of individual cell type fluctuate, there existed extensive and robust correlations/anti-correlations between cell types, exemplified by the pronounced anti-correlation between blood neutrophils and B cells. Through longitudinal study of the blood content of healthy mice, we found that leukocyte fluctuations are ergodic yet subject to chaotic behaviors characterized by a broad spectrum of characteristic timescales. We then built a minimal mathematical model to capture these dynamical features of hematopoiesis (fluctuations, correlations, and chaos) and explain how the accumulation of B cells (e.g. during lymphoma development) would transition the blood cell dynamics from chaos to oscillations (as observed clinically). Finally, we demonstrated the ubiquity and consistency of the correlated fluctuations in hematopoiesis by comparing mouse cohorts of different genetic backgrounds and ages. To conclude, we discuss how study of hematopoiesis must factor in the newfound chaotic dynamics at homeostasis, towards better modeling the responses to perturbations.

Machine learning based battery pack health prediction using real-world data

The complex operational conditions in real-world electric vehicles (EVs) contribute to the complexity of managing and maintaining battery packs. Adding to these challenges is the intricate task of modeling the inconsistent coupling among individual cells within these packs. This study addresses the ongoing challenges in modeling lithium-ion battery (LIB) cells within packs and estimating their state of health (SOH) for practical applications. This research proposed a PCA-CNN-Transformer method to model and predict the SOH model of real-world EV. Three main contributions are presented: a novel approach to defining an attenuation SOH model based on delivered energy, a methodology utilizing Principal Component Analysis (PCA) for cell modeling, and an SOH estimation model employing CNN-Transformer architecture. To address both pack and cell-level modeling, a hierarchical feature extraction approach is proposed. The health features extracted from both levels are assessed using grey relational analysis, showing a strong correlation with LIB SOH, exceeding 0.70. The proposed cell modeling method significantly reduces data size by 96%, enhancing computational efficiency. Furthermore, the integration of 1D-CNN in the SOH estimation model overcomes the limitations of the attention mechanism, achieving a MAE with 0.0406 and r-square of 0.9327, improved the original transformer network performance by 10.95%. This study also examines and discusses the performance of the informer and transformer models, elaborating why the informer model underperformed in this dataset.

Exploring phosphate impact on arsenate uptake and distribution in freshwater phytoplankton: Insights from single-cell ICP-MS

In this study, we investigated the interaction between arsenate (AsV) and phosphate (PO43−) in freshwater phytoplankton using single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS). This study aimed to elucidate the influence of varying PO43− concentrations on arsenic (As) uptake and distribution at the single-cell level, providing insights into intraspecies diversity. Two species of freshwater phytoplanktons, Scenedesmus acutus and Pediastrum duplex, were cultured under different concentrations of PO43− and AsV in a controlled laboratory environment. Scenedesmus acutus, a species with strong salt tolerance, and Pediastrum duplex, known for its weak salt tolerance, were selected based on their contrasting behaviors in previous studies. SC-ICP-MS revealed non-uniform uptake of As by individual phytoplankton cells, with distinct variations in response to PO43− availability. Arsenic uptake by both species declined with a high PO43− level after 7 days of exposure. However, after 14 days, As uptake increased in S. acutus with higher PO43− concentrations, but decreased in P. duplex. Moreover, our findings revealed differences in cell morphology and membrane integrity between the two species in response to AsV and various PO43− concentrations. S. acutus maintained cell integrity under all experimental culture conditions, whereas P. duplex experienced cell lysis at elevated AsV and PO43− concentrations. This study highlights the varying responses of freshwater phytoplankton to changes in AsV and PO43− levels and underscores the advantages of SC-ICP-MS over conventional ICP-MS in providing detailed, cellular level insights. These findings are crucial for understanding and managing As pollution in aquatic ecosystems.

A single-cell atlas of the Culex tarsalis midgut during West Nile virus infection.

The mosquito midgut functions as a key interface between pathogen and vector. However, studies of midgut physiology and associated virus infection dynamics are scarce, and in Culex tarsalis - an extremely efficient vector of West Nile virus (WNV) - nonexistent. We performed single-cell RNA sequencing on Cx. tarsalis midguts, defined multiple cell types, and determined whether specific cell types are more permissive to WNV infection. We identified 20 cell states comprised of 8 distinct cell types, consistent with existing descriptions of Drosophila and Aedes aegypti midgut physiology. Most midgut cell populations were permissive to WNV infection. However, there were higher levels of WNV RNA (vRNA) in enteroendocrine cells and cells enriched for mitochondrial genes, suggesting enhanced replication in these populations. In contrast, proliferating intestinal stem cell (ISC) populations had the lowest levels of vRNA, a finding consistent with studies suggesting ISC proliferation in the midgut is involved in viral control. Notably, we did not detect significant WNV-infection induced upregulation of canonical mosquito antiviral immune genes (e.g., AGO2, R2D2, etc.) at the whole-midgut level. Rather, we observed a significant positive correlation between immune gene expression levels and vRNA in individual cells, suggesting that within midgut cells, high levels of vRNA may trigger antiviral responses. Our findings establish a Cx. tarsalis midgut cell atlas, and provide insight into midgut infection dynamics of WNV by characterizing cell-type specific enhancement/restriction of, and immune response to, infection at the single-cell level.

Overcoming fixation and permeabilization challenges in flow cytometry by optical barcoding and multi-pass acquisition.

The fixation and permeabilization of cells are essential for labeling intracellular biomarkers in flow cytometry. However, these chemical treatments often alter fragile targets, such as cell surface and fluorescent proteins, and can destroy chemically-sensitive fluorescent labels. This reduces measurement accuracy and introduces compromises into sample workflows, leading to losses in data quality. Here, we demonstrate a novel multi-pass flow cytometry approach to address this long-standing problem. Our technique utilizes individual cell barcoding with laser particles, enabling sequential analysis of the same cells with single-cell resolution maintained. Chemically-fragile protein markers and their fluorochrome conjugates are measured prior to destructive sample processing and adjoined to subsequent measurements of intracellular markers after fixation and permeabilization. We demonstrate the effectiveness of our technique in accurately measuring intracellular fluorescent proteins and methanol-sensitive antigens and fluorophores, along with various surface and intracellular markers. This approach significantly enhances assay flexibility, enabling accurate and comprehensive cell analysis without the constraints of conventional one-time measurement flow cytometry. This innovation paves new avenues in flow cytometry for a wide range of applications in immuno-oncology, stem cell research, and cell biology.

Impact of environmental concentrations of fipronil on DNA integrity and brain structure of Bombus atratus bumblebees

Fipronil (FP) is an insecticide used in the treatment and control of pests, but it also adversely affects bees. Currently, there is no data on the genotoxic effects of FP in the brain of bumblebees. Thus, through the comet assay and routine morphological analysis, we analyzed the morphological effects and potential genotoxicity of environmentally relevant concentrations of FP on the brain of Bombus atratus. Bumblebees were exposed at concentrations of 2.5 μg/g and 3.5 μg/g for 96 hours. After the exposure, the brains were removed for morphological and morphometric analysis, and the comet assay procedure - used to detect DNA damage in individual cells using electrophoresis. Our data showed that both concentrations (2.5 μg/g and 3.5 μg/g) caused DNA damage in brain cells. These results corroborate the morphological data. We observed signs of synapse loss in the calyx structure, intercellular spaces between compact inner and non-compact inner cells, and cell swelling. This study provides unprecedented evidence of the effects of FP on DNA and cellular structures in the brain of B. atratus and reinforces the need to elucidate its toxic effects on other species to allow future risk assessments and conservation projects.

OpenNucleome for high-resolution nuclear structural and dynamical modeling.

The intricate structural organization of the human nucleus is fundamental to cellular function and gene regulation. Recent advancements in experimental techniques, including high-throughput sequencing and microscopy, have provided valuable insights into nuclear organization. Computational modeling has played significant roles in interpreting experimental observations by reconstructing high-resolution structural ensembles and uncovering organization principles. However, the absence of standardized modeling tools poses challenges for furthering nuclear investigations. We present OpenNucleome-an open-source software designed for conducting GPU-accelerated molecular dynamics simulations of the human nucleus. OpenNucleome offers particle-based representations of chromosomes at a resolution of 100 KB, encompassing nuclear lamina, nucleoli, and speckles. This software furnishes highly accurate structural models of nuclear architecture, affording the means for dynamic simulations of condensate formation, fusion, and exploration of non-equilibrium effects. We applied OpenNucleome to uncover the mechanisms driving the emergence of 'fixed points' within the nucleus-signifying genomic loci robustly anchored in proximity to specific nuclear bodies for functional purposes. This anchoring remains resilient even amidst significant fluctuations in chromosome radial positions and nuclear shapes within individual cells. Our findings lend support to a nuclear zoning model that elucidates genome functionality. We anticipate OpenNucleome to serve as a valuable tool for nuclear investigations, streamlining mechanistic explorations and enhancing the interpretation of experimental observations.

OpenNucleome for high-resolution nuclear structural and dynamical modeling

The intricate structural organization of the human nucleus is fundamental to cellular function and gene regulation. Recent advancements in experimental techniques, including high-throughput sequencing and microscopy, have provided valuable insights into nuclear organization. Computational modeling has played significant roles in interpreting experimental observations by reconstructing high-resolution structural ensembles and uncovering organization principles. However, the absence of standardized modeling tools poses challenges for furthering nuclear investigations. We present OpenNucleome—an open-source software designed for conducting GPU-accelerated molecular dynamics simulations of the human nucleus. OpenNucleome offers particle-based representations of chromosomes at a resolution of 100 KB, encompassing nuclear lamina, nucleoli, and speckles. This software furnishes highly accurate structural models of nuclear architecture, affording the means for dynamic simulations of condensate formation, fusion, and exploration of non-equilibrium effects. We applied OpenNucleome to uncover the mechanisms driving the emergence of ‘fixed points’ within the nucleus—signifying genomic loci robustly anchored in proximity to specific nuclear bodies for functional purposes. This anchoring remains resilient even amidst significant fluctuations in chromosome radial positions and nuclear shapes within individual cells. Our findings lend support to a nuclear zoning model that elucidates genome functionality. We anticipate OpenNucleome to serve as a valuable tool for nuclear investigations, streamlining mechanistic explorations and enhancing the interpretation of experimental observations.

A Droplet-Based Microfluidic Platform for High-Throughput Culturing of Yeast Cells in Various Conditions.

Yeast plays a significant role in a variety of fields. In particular, it is extensively used as a model organism in genetics and cellular biology studies, and is employed in the production of vaccines, pharmaceuticals, and biofuels. Traditional "bulk"-based studies on yeast growth often overlook cellular variability, emphasizing the need for single-cell analysis. Micro-droplets, tiny liquid droplets with high surface-area-to-volume ratios, offer a promising platform for investigating single or a small number of cells, allowing precise control and monitoring of individual cell behaviors. Microfluidic devices, which facilitate the generation of micro-droplets, are advantageous due to their reduced volume requirements and ability to mimic in vivo micro-environments. This study introduces a custom-designed microfluidic device to encapsulate yeasts in micro-droplets under various conditions in a parallel manner. The results reveal that optimal glucose concentrations promoted yeast growth while cycloheximide and Cu2+ ions inhibited it. This platform enhances yeast cultivation strategies and holds potential for high-throughput single-cell investigations in more complex organisms.

Evolutionary stability of developmental commitment

Evolution of unicellular to multicellular organisms must resolve conflicts in reproductive interests between individual cells and the group. The social amoeba Dictyostelium discoideum is a soil-living eukaryote with facultative sociality. While cells grow in the presence of nutrients, cells aggregate under starvation to form fruiting bodies containing spores and altruistic stalk cells. Once cells socially committed, they complete formation of fruiting bodies, even if a new source of nutrients becomes available. The persistence of this social commitment raises questions as it inhibits individual cells from swiftly returning to solitary growth. I hypothesize that traits enabling premature de-commitment are hindered from being selected. Recent work has revealed outcomes of the premature de-commitment through forced refeeding; The de-committed cells take an altruistic prestalk-like position due to their reduced cohesiveness through interactions with socially committed cells. I constructed an evolutionary model assuming their division of labor. The results revealed a valley in the fitness landscape that prevented invasion of de-committing mutants, indicating evolutionary stability of the social commitment. The findings provide a general scheme that maintains multicellularity by evolving a specific division of labor, in which less cohesive individuals become altruists.

PURE-seq identifies Egr1 as a Potential Master Regulator in Murine Aging by Sequencing Long-Term Hematopoietic Stem Cells.

Single-cell transcriptomics is valuable for uncovering individual cell properties, particularly in highly heterogeneous systems. However, this technique often results in the analysis of many well-characterized cells, increasing costs and diluting rare cell populations. To address this, we developed PURE-seq (PIP-seq for Rare-cell Enrichment and Sequencing) for scalable sequencing of rare cells. PURE-seq allows direct cell loading from FACS into PIP-seq reactions, minimizing handling and reducing cell loss. PURE-seq reliably captures rare cells, with 60 minutes of sorting capturing tens of cells at a rarity of 1 in 1,000,000. Using PURE-seq, we investigated murine long-term hematopoietic stem cells and their transcriptomes in the context of hematopoietic aging, identifying Egr1 as a potential master regulator of hematopoiesis in the aging context. PURE-seq offers an accessible and reliable method for isolating and sequencing cells that are currently too rare to capture successfully with existing methods.