Heterogeneity Of Individual Cells Research Articles

Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis.

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

Genetic and phenotypic profiling of single living circulating tumor cells from patients with microfluidics

Accurate prediction of the efficacy of immunotherapy for cancer patients through the characterization of both genetic and phenotypic heterogeneity in individual patient cells holds great promise in informing targeted treatments, and ultimately in improving care pathways and clinical outcomes. Here, we describe the nanoplatform for interrogating living cell host-gene and (micro-)environment (NICHE) relationships, that integrates micro- and nanofluidics to enable highly efficient capture of circulating tumor cells (CTCs) from blood samples. The platform uses a unique nanopore-enhanced electrodelivery system that efficiently and rapidly integrates stable multichannel fluorescence probes into living CTCs for in situ quantification of target gene expression, while on-chip coculturing of CTCs with immune cells allows for the real-time correlative quantification of their phenotypic heterogeneities in response to immune checkpoint inhibitors (ICI). The NICHE microfluidic device provides a unique ability to perform both gene expression and phenotypic analysis on the same single cells in situ, allowing us to generate a predictive index for screening patients who could benefit from ICI. This index, which simultaneously integrates the heterogeneity of single cellular responses for both gene expression and phenotype, was validated by clinically tracing 80 non-small cell lung cancer patients, demonstrating significantly higher AUC (area under the curve) (0.906) than current clinical reference for immunotherapy prediction.

A comparison of marker gene selection methods for single-cell RNA sequencing data

BackgroundThe development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalog and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper, we benchmark 59 computational methods for selecting marker genes in scRNA-seq data.ResultsWe compare the performance of the methods using 14 real scRNA-seq datasets and over 170 additional simulated datasets. Methods are compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed, and their implementation quality. In addition, various case studies are used to scrutinize the most commonly used methods, highlighting issues and inconsistencies.ConclusionsOverall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student’s t-test, and logistic regression.

A mesoporous gold biosensor to investigate immune checkpoint protein heterogeneity in single lung cancer cells

Immune checkpoint proteins (ICPs) play a major role in a patient's immune response against cancer. Tumour cells usually express those proteins to communicate with immune cells as a process of escaping the anti-cancer immune response. Detecting the major functional immune checkpoint proteins present on cancer cells (such as circulating tumor cells or CTCs) and examining the heterogeneity in their expression at the single-cell level could play a crucial role in both cancer diagnosis and the monitoring of therapy. In this study, we develop a mesoporous gold biosensor to precisely assess ICP heterogeneity in individual cancer cells within a lung cancer model. The platform utilizes a nanostructured mesoporous gold surface to capture CTCs and a Surface Enhanced Raman Scattering (SERS) readout to identify and monitor the expression of key ICP proteins (PD-L1, B7H4, CD276, CD80) in lung cancer cells. The homogeneous and abundant pores in mesoporous 3D gold nanostructures enable increased antibody loading on-chip and an enhanced SERS signal, which are key to our single cell capture, and accurate analysis of ICPs in cancer cells with high sensitivity. Our lung cancer cell line model data showed that our method can detect single cells and analyse the expression of four lung cancer associated ICPs on individual cell surfaces during treatment. To show the potential of our mesoporous gold biosensor in analysing clinical samples, we tested 9 longitudinal Peripheral Blood Mononuclear Cells (PBMC) samples from lung cancer patient before and after therapy. Our mesoporous biosensor successfully captured single CTCs and found that the expression of ICPs in CTCs is highly heterogeneous in both pre-treatment and treated PBMC samples isolated from lung cancer patient blood. We suggest that our findings will help clinicians in selecting the most appropriate therapy for patients.

Cofea: correlation-based feature selection for single-cell chromatin accessibility data.

Single-cell chromatin accessibility sequencing (scCAS) technologies have enabled characterizing the epigenomic heterogeneity of individual cells. However, the identification of features of scCAS data that are relevant to underlying biological processes remains a significant gap. Here, we introduce a novel method Cofea, to fill this gap. Through comprehensive experiments on 5 simulated and 54 real datasets, Cofea demonstrates its superiority in capturing cellular heterogeneity and facilitating downstream analysis. Applying this method to identification of cell type-specific peaks and candidate enhancers, as well as pathway enrichment analysis and partitioned heritability analysis, we illustrate the potential of Cofea to uncover functional biological process.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

ScSmOP: a universal computational pipeline for single-cell single-molecule multiomics data analysis.

Single-cell multiomics techniques have been widely applied to detect the key signature of cells. These methods have achieved a single-molecule resolution and can even reveal spatial localization. These emerging methods provide insights elucidating the features of genomic, epigenomic and transcriptomic heterogeneity in individual cells. However, they have given rise to new computational challenges in data processing. Here, we describe Single-cell Single-molecule multiple Omics Pipeline (ScSmOP), a universal pipeline for barcode-indexed single-cell single-molecule multiomics data analysis. Essentially, the C language is utilized in ScSmOP to set up spaced-seed hash table-based algorithms for barcode identification according to ligation-based barcoding data and synthesis-based barcoding data, followed by data mapping and deconvolution. We demonstrate high reproducibility of data processing between ScSmOP and published pipelines in comprehensive analyses of single-cell omics data (scRNA-seq, scATAC-seq, scARC-seq), single-molecule chromatin interaction data (ChIA-Drop, SPRITE, RD-SPRITE), single-cell single-molecule chromatin interaction data (scSPRITE) and spatial transcriptomic data from various cell types and species. Additionally, ScSmOP shows more rapid performance and is a versatile, efficient, easy-to-use and robust pipeline for single-cell single-molecule multiomics data analysis.

Panhematopoietic RNA barcoding enables kinetic measurements of nucleate and anucleate lineages and the activation of myeloid clones following acute platelet depletion

BackgroundPlatelets and erythrocytes constitute over 95% of all hematopoietic stem cell output. However, the clonal dynamics of HSC contribution to these lineages remains largely unexplored.ResultsWe use lentiviral genetic labeling of mouse hematopoietic stem cells to quantify output from all lineages, nucleate, and anucleate, simultaneously linking these with stem and progenitor cell transcriptomic phenotypes using single-cell RNA-sequencing. We observe dynamic shifts of clonal behaviors through time in same-animal peripheral blood and demonstrate that acute platelet depletion shifts the output of multipotent hematopoietic stem cells to the exclusive production of platelets. Additionally, we observe the emergence of new myeloid-biased clones, which support short- and long-term production of blood cells.ConclusionsOur approach enables kinetic studies of multi-lineage output in the peripheral blood and transcriptional heterogeneity of individual hematopoietic stem cells. Our results give a unique insight into hematopoietic stem cell reactivation upon platelet depletion and of clonal dynamics in both steady state and under stress.

Single-cell sequencing technology in colorectal cancer: a new technology to disclose the tumor heterogeneity and target precise treatment.

Colorectal Cancer (CRC) is one of the most common gastrointestinal tumors, and its high tumor heterogeneity makes traditional sequencing methods incapable of obtaining information about the heterogeneity of individual cancer cells in CRC. Therefore, single-cell sequencing technology can be applied to better analyze the differences in genetic and protein information between cells, to obtain genomic sequence information of single cells, and to more thoroughly analyze the cellular characteristics and interactions in the CRC microenvironment. This will provide a more comprehensive understanding of colorectal cancer development and metastasis and indicate the treatment plan and prognosis. In this study, we review the application of single-cell sequencing to analyze the tumor microenvironment of CRC, explore the mechanisms involved in CRC metastasis and progression, and provide a reference for potential treatment options.

Gene function and cell surface protein association analysis based on single-cell multiomics data

Single-cell transcriptomics provides researchers with a powerful tool to resolve the transcriptome heterogeneity of individual cells. However, this method falls short in revealing cellular heterogeneity at the protein level. Previous single-cell multiomics studies have focused on data integration rather than exploiting the full potential of multiomics data. Here we introduce a new analysis framework, gene function and protein association (GFPA), that mines reliable associations between gene function and cell surface protein from single-cell multimodal data. Applying GFPA to human peripheral blood mononuclear cells (PBMCs), we observe an association of epithelial mesenchymal transition (EMT) with the CD99 protein in CD4 T cells, which is consistent with previous findings. Our results show that GFPA is reliable across multiple cell subtypes and PBMC samples. The GFPA python packages and detailed tutorials are freely available at https://github.com/studentiz/GFPA.

The magnitude of heterogeneity in individual-cell growth dynamics is an inherent characteristic of Salmonella enterica ser. Typhimurium strains

Individual-cell heterogeneity is a major source of variability in biological systems affecting importantly, among others, microbial behavior. Characterization of cell populations of pathogenic bacterial strains in their entirety, ignoring the phenotypic variability of single cells, may result in erroneous safety risk estimates. The objective of the present study was the evaluation and comparison of the heterogeneity in the individual-cell growth dynamics of different strains of Salmonella enterica. The stochasticity in the growth of single cells of five S. enterica ser. Typhimurium strains was quantitatively described using time-lapse microscopy, and the existence of a strain effect was statistically assessed. In total, 831 growing microcolonies originating from single cells were monitored and analyzed, and the growth kinetic parameters of lag time (λ) and maximum specific growth rate (μmax) for each one of them were estimated. An extensive heterogeneity in individual-cell growth kinetics was recorded, while significant inter-strain differences in their heterogeneity were evident based on simultaneous Bonferroni confidence intervals and Levene’s tests. The Logistic and LogLogistic probability distribution provided the best fitting for μmax and λ data, respectively for all the tested strains. The strain effect on the above distributions was also demonstrated with pairwise comparisons of the decile differences. The impact of strain-dependent heterogeneity on microbial growth was visualized by comparing stochastic growth curves of different strains using Monte Carlo simulation. In conclusion, the individual-cell growth dynamics of S. enterica are heterogeneous, with the magnitude of the observed heterogeneity appearing to be an inherent characteristic of bacterial strains.

Fungal resilience and host-pathogen interactions: Future perspectives and opportunities.

We are constantly exposed to the threat of fungal infection. The outcome-clearance, commensalism or infection-depends largely on the ability of our innate immune defences to clear infecting fungal cells versus the success of the fungus in mounting compensatory adaptive responses. As each seeks to gain advantage during these skirmishes, the interactions between host and fungal pathogen are complex and dynamic. Nevertheless, simply compromising the physiological robustness of fungal pathogens reduces their ability to evade antifungal immunity, their virulence, and their tolerance against antifungal therapy. In this article I argue that this physiological robustness is based on a 'Resilience Network' which mechanistically links and controls fungal growth, metabolism, stress resistance and drug tolerance. The elasticity of this network probably underlies the phenotypic variability of fungal isolates and the heterogeneity of individual cells within clonal populations. Consequently, I suggest that the definition of the fungal Resilience Network represents an important goal for the future which offers the clear potential to reveal drug targets that compromise drug tolerance and synergise with current antifungal therapies.

High-throughput imaging of mRNA at the single-cell level in human primary immune cells.

Measurement of gene expression at the single-cell level has advanced the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for high-content imaging (HCI)-based quantification of relative changes in transcript abundance at the single-cell level in human primary immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, named hcHCR, combines the sensitivity of the hybridization chain reaction (HCR) for the visualization of RNA in single cells, with the speed, scalability, and reproducibility of HCI. We first tested eight cell attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in 384-well format and documented the ability of the method to detect changes in transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of three transcripts per cell at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be suitable for low- to medium-throughput chemical or functional genomics screens in primary human cells, with the possibility of performing screens on cells obtained from patients with a specific disease.

Machine learning approach for predicting single cell lag time of Salmonella Enteritidis after heat and chlorine treatment

The importance of single-cell variability is increasingly prominent with the developments in foodborne pathogens modeling. Traditional predictive microbiology model cannot accurately describe the growth behavior of small numbers of cells due to individual cell heterogeneity. The objective of the present study was to develop predictive models for single cell lag times of Salmonella Enteritidis after heat and chlorine treatment. A time-lapse microscopy method was employed to evaluate the single cell lag time by monitoring cell divisions. Four supervised machine learning algorithms including gradient boosting regression tree (GBRT), artificial neural network (ANN), random forest (RF), and support vector regression (SVR) were applied and compared. Results show that all four machine learning models have good predictive capabilities without an overfitting of the data. The ANN approach demonstrated superior prediction performance over other machine learning models (RMSE: 0.209, MAE: 0.135 and R2: 0.989). Furthermore, the SHapley Additive exPlanation (SHAP) measures were used to capture the influence of each feature on the model output, and results revealed that population lag times and sublethal injury rate have dominant impacts on the single cell lag time. Consequently, the findings generated from this study may be useful in managing the potential food safety risk caused by single cells of foodborne pathogens.

Dynamic metabolic change of cancer cells induced by natural killer cells at the single-cell level studied by label-free mass cytometry

Natural killer cells (NK cells) are important immune cells which have attracted increasing attention in cancer immunotherapy. Due to the heterogeneity of cells, individual cancer cells show different resistance to NK cytotoxicity, which has been revealed by flow cytometry. Here we used label-free mass cytometry (CyESI-MS) as a new tool to analyze the metabolites in Human Hepatocellular Carcinoma (HepG2) cells at the single-cell level after the interaction with different numbers of NK92 MI cells. A large amount of chemical information from individual HepG2 cells was obtained showing the process of cell apoptosis induced by NK cells. Nineteen metabolites which consecutively change during cell apoptosis were revealed by calculating their average relative intensity. Four metabolic pathways were impacted during cell apoptosis which hit 4 metabolites including glutathione (GSH), creatine, glutamic acid and taurine. We found that the HepG2 cells could be divided into two phenotypes after co-culturing with NK cells according to the bimodal distribution of concentration of these 4 metabolites. The correlation between metabolites and different apoptotic pathways in the early apoptosis cell group was established by the 4 metabolites at the single-cell level. This is a new idea of using single-cell specific metabolites to reveal the metabolic heterogeneity in cell apoptosis which would be a powerful means for evaluating the cytotoxicity of NK cells.

Miniaturized bioluminescence technology for single-cell quantification of caspase-3/7

Correct determination of the instantaneous level and changes of relevant proteins inside individual cells is essential for correct interpretation and understanding of physiological, diagnostic, and therapeutic events. Thus, single-cell analyses are important for quantification of natural cellular heterogeneity, which cannot be evaluated from averaged data of a cell population measurements. Here, we developed an original highly sensitive and selective instrumentation and methodology based on homogeneous single-step bioluminescence assay to quantify caspases and evaluate their heterogeneity in individual cells. Individual suspended cells are selected under microscope and reliably transferred into the 7 µl detection vials by a micromanipulator. The sensitivity of the method is given by implementation of photomultiplying tube with a cooled photocathode working in the photon counting mode. By optimization of our device and methodology, the limits of detection and quantitation were decreased down to 2.1 and 7.0 fg of recombinant caspase-3, respectively. These masses are lower than average amounts of caspase-3/7 in individual apoptotic and even non-apoptotic cells. As a proof of concept, the content of caspase-3/7 in single treated and untreated HeLa cells was determined to be 154 and 25 fg, respectively. Based on these results, we aim to use the technology for investigations of non-apoptotic functions of caspases.

Nanofluidics for sub-single cellular studies: Nascent progress, critical technologies, and future perspectives

In the field of cell studies, there is a burgeoning trend to further downscale the investigation from a single-cell level to a sub-single-cell level. Subcellular matter is the basic content in cells and correlates with cell heterogeneity. Sub-single cellular studies focus on the subcellular matter in single cells and aim to understand the details and heterogeneity of individual cells in terms of the subcellular matter or even at the single component/vesicle/molecule level. Hence, sub-single cellular studies can provide deeper insights into fundamental cell biology and the development of new diagnostic and therapeutic technologies and applications. Nonetheless, the contents of a single cell are not only ultra-small in volume but also extremely complex in composition, far exceeding the capabilities of most tools used in current cell studies. We believe that nanofluidics holds great potential in providing ideal tools for sub-single cellular studies, not only because of their capability to handle femtoliter/attoliter-scale samples, but also because of their possibility to manipulate and analyze subcellular matters at the single component/vesicle/molecule level in a high-throughput manner. In this review, we summarize the efforts in the field of nanofluidics for sub-single cellular studies, focusing on nascent progress and critical technologies that have the potential to overcome the technical bottlenecks. Some challenges and future opportunities to integrate with information sciences are also discussed.

A single-cell analytical approach to quantify activated caspase-3/7 during osteoblast proliferation, differentiation, and apoptosis.

The protein heterogeneity at the single-cell level has been recognized to be vital for an understanding of various life processes during animal development. In addition, the knowledge of accurate quantity of relevant proteins at cellular level is essential for appropriate interpretation of diagnostic and therapeutic results. Some low-copy-number proteins are known to play a crucial role during cell proliferation, differentiation, and also in apoptosis. The fate decision is often based on the concentration of these proteins in the individual cells. This is likely to apply also for caspases, cysteine proteases traditionally associated with cell death via apoptosis but recently being discovered also as important factors in cell proliferation and differentiation. The hypothesis was tested in bone-related cells, where modulation of fate from apoptosis to proliferation/differentiation and vice versa is particularly challenging, e.g., towards anti-osteoporotic treatments and anti-cancer strategies. An ultrasensitive and highly selective method based on bioluminescence photon counting was used to quantify activated caspase-3/7 in order to demonstrate protein-level heterogeneity in individual cells within one population and to associate quantitative measurements with different cell fates (proliferation, differentiation, apoptosis). The results indicate a gradual increase of caspase-3/7 activation from the proliferative status to differentiation (more than three times) and towards apoptosis (more than six times). The findings clearly support one of the putative key mechanisms of non-apoptotic functions of pro-apoptotic caspases based on fine-tuning of their activation levels.

Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data.

High-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell's heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR). In scRNA-seq data, ATV-LRR can reconstruct the low-rank subspace structure to learn the similarity of cells. The low-rank representation can not only segment multiple linear subspaces, but also extract important information. Moreover, adaptive total variation also can remove cell noise and preserve cell feature details by learning the gradient information of the data. At the same time, to analyze scRNA-seq data with unknown prior information, we introduced the maximum eigenvalue method into the ATV-LRR model to automatically identify cell populations. The final clustering results show that the ATV-LRR model can detect cell types more effectively and stably.

Adhesion heterogeneity of individual bacterial cells in an axenic culture studied by atomic force microscopy.

The evaluation of bacterial adhesive properties at a single-cell level is critical for under standing the role of phenotypic heterogeneity in bacterial attachment and community formation. Bacterial population exhibits a wide variety of adhesive properties at the single-cell level, suggesting that bacterial adhesion is a rather complex process and some bacteria are prone to phenotypic heterogeneity. This heterogeneity was more pronounced for Escherichia coli, where two subpopulations were detected. Subpopulations exhibiting higher adhesion forces may be better adapted to colonize a new surface, especially during sudden changes in environmental conditions. Escherichia coli was characterized by a higher adhesion force, a stronger ability to form biofilm and larger heterogeneity index calculated in comparison with Bacillus subtilis. Higher adhesion forces are associated with a more efficient attachment of bacteria observed in an adhesion assay and might provide a basis for successful colonization, survival and multiplications in changing environment. The atomic force microscopy provides a platform for investigation of the adhesion heterogeneity of individual cells within a population, which may be expected to underpin further elucidation of the adaptive significance of phenotypic heterogeneity in a natural environment.