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PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

PubMed1995

Identification of receptor ligands and receptor subtypes using antagonists in a capillary electrophoresis single-cell biosensor separation system.

A capillary electrophoresis system with single-cell biosensors as a detector has been used to separate and identify ligands in complex biological samples. The power of this procedure was significantly increased by introducing antagonists that inhibited the cellular response from selected ligand-receptor interactions. The single-cell biosensor was based on the ligand-receptor binding and G-protein-mediated signal transduction pathways in PC12 and NG108-15 cell lines. Receptor activation was measured as increases in cytosolic free calcium ion concentration by using fluorescence microscopy with the intracellular calcium ion indicator fluo-3-acetoxymethyl ester. Specifically, a mixture of bradykinin (BK) and acetylcholine (ACh) was fractionated and the components were identified by inhibiting the cellular response with icatibant (HOE 140), a selective antagonist to the BK B2 receptor subtype (B2BK), and atropine, an antagonist to muscarinic ACh receptor subtypes. Structurally related forms of BK were also identified based on inhibiting B2BK receptors. Applications of this technique include identification of endogenous BK in a lysate of human hepatocellular carcinoma cells (Hep G2) and screening for bioactivity of BK degradation products in human blood plasma. The data demonstrate that the use of antagonists with a single-cell biosensor separation system aids identification of separated components and receptor subtypes.

Animals↗

PubMed2000

[Single cell analysis of H(2)O(2) mediated Fas mRNA expression and Ca(2+) influx in LO(2) cells].

OBJECTIVE: To investigate whether apoptotic injury is induced by Fas mRNA expression mediated by H(2)O(2) and concerned with intracellular Ca(2+) unbalance at a single cell level. METHODS: Fas mRNA expression in human hepatocyte (LO(2) cell) was analyzed by whole-cell patch-clamp combined with single cell reverse transcription polymerase chain reaction (RT-PCR). Cytosolic Ca(2+) concentration was measured with another whole-cell patch-clamp combined with single-cell microfluorescence [Ca(2+)] i measurement at the same time. The index of early phase apoptotic cells (Annexin-V(+) cells) measured with flow cytometry (FCM) and the hepatocytic morphological changes were observed by scanning electron microscopy. RESULTS: Fas mRNA of LO(2) cells cultured with H(2)O(2) (10 micromol/L) was expressed at 2 h. The single cell [Ca(2+)] i analyzed by another whole-cell patchclamp increased to 1 115 nmol/L +/- 227 nmol/L (P < 0.01, t = 13.37); at the same time, an elevated Ca(2+) wave was recorded. The index of Annexin-V(+) cells was as high as 16.18 +/- 0.65 (P < 0.01, t = 48.41), and the membrane surface protuberances were observed. CONCLUSIONS: The activation of Fas mRNA, as a major apoptotic factor mediated by H(2)O(2) in LO(2) cells, is probably associated with the imbalance of intracellular Ca(2+).

Apoptosis↗

PubMed2024

SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Single-Cell Analysis↗

PubMed2026

CERTOMICS: trusted single-cell multiomics pipeline for high-resolution profiling of adoptive cellular immunotherapies.

SUMMARY: Adoptive cellular immunontherapies, such as chimeric antigen receptor (CAR) T cell therapy, have transformed cancer treatment, yet challenges such as resistance, relapse, and high costs limit their efficacy and accessibility. A comprehensive understanding of cellular heterogeneity and molecular profiles is essential to improve these therapies. Advanced single-cell multiomics technologies have the power to analyze the complex interactions between CAR-engineered cells, immune cells, and tumor cells. However, standardized single-cell multiomics computational pipelines specifically tailored to CAR-engineered cell products are lacking. Due to the synthetic nature of CAR transgenes, additional steps for reliable identification and characterization of CAR-positive cells are required but not included in existing data-processing workflows. To address this, we present CERTOMICS, a Nextflow-based, CAR-aware pipeline offering enhanced CERTainty in immunophenotyping and data interpretation, tailored for single-cell multiOMICSprofiling of adoptive cellular immunotherapies. The pipeline standardizes processing 10x Genomics single-cell multiomics data and integrates CAR-specific identification and quality control. Additionally, a curated repository of CAR construct sequences and annotation data is provided, serving as an extensible resource to support the analysis and development of CAR T cell therapies. AVAILABILITY AND IMPLEMENTATION: Detailed documentation of this pipeline, along with a resource on latest FDA-approved CAR therapies is available on our website: https://fraunhofer-izi.github.io/Living-Drugs-Wiki/. The data underlying this article are available on GitHub at https://github.com/fraunhofer-izi/CERTOMICS. The code is also published on Zenodo at https://doi.org/10.5281/zenodo.18709693.

Multiomics↗

PubMed2026

CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data.

MOTIVATION: Chromatin 3D folding creates numerous DNA interactions, participating in gene expression regulation. Single-cell chromatin-accessibility assays now profile hundreds of thousands of cells, challenging existing methods for mapping cis-regulatory interactions. RESULTS: We present CIRCE, a fast and scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data. CIRCE re-implements the Cicero workflow to analyse single-cell atlases, cutting runtime and memory use by several orders of magnitude. We also provide new options to compute metacells, grouping similar cells to reduce data sparsity. We benchmarked CIRCE against Cicero on two datasets of different sizes and demonstrated the improvement from CIRCE's metacells' strategy with promoter capture Hi-C data. We also evaluated how DNA interaction predictions are impacted by different pre-processing. We observed a negative impact of Cicero's count normalization, and the best performance was obtained with the single-cell count matrix directly. Finally, we demonstrated the scalability of CIRCE by processing a dataset of more than 700 000 cells and 1 million DNA regions in less than an hour. CIRCE should greatly facilitate the prediction of DNA region interactions for scverse and Python users, while providing new and up-to-date pre-processing insights. AVAILABILITY AND IMPLEMENTATION: CIRCE is released as an open-source software under the AGPL-3.0 licence. The package source code is available on GitHub at https://github.com/cantinilab/CIRCE, and its documentation is accessible at https://circe.readthedocs.io. The code to reproduce the presented results is available as a Snakemake pipeline at https://github.com/cantinilab/circe_reproducibility.s.

Software↗

PubMed2026

The clinical promise of mass spectrometry-based single-cell proteomics: from bedside to bench.

INTRODUCTION: Single-cell proteomics (SCP) is entering into a transformative phase, moving beyond technically demanding benchmarking studies toward robust and reproducible workflows capable of quantifying thousands of proteins per cell. These advances highlight SCP's potential to address clinically relevant questions by resolving cellular and pathological heterogeneity that remains obscured in bulk proteomics. AREAS COVERED: This review discusses current advances, challenges, and clinical applications of SCP based on literature identified through searches in major scientific databases. Many clinically relevant samples remain underexplored in SCP studies, in part because their application requires careful evaluation of pre-analytical variables that can strongly influence proteomic readouts. Current SCP methodologies vary according to sample type, experimental conditions, and available resources. Compared with single-cell RNA sequencing, SCP remains limited in cellular throughput, making it challenging to define optimal sample sizes and to reliably detect both abundant and rare cell populations. These limitations also make dataset integration difficult, as reduced cellular coverage and sampling depth increase data sparsity. Moreover, implementing quality control strategies across sequential SCP experiments is essential to ensure data robustness, comparability, and accurate biological interpretation. EXPERT OPINION: Applying SCP to clinical samples advances our understanding of biological complexity and holds potential to drive progress in translational and precision medicine.

Humans↗

PubMed2026

Amaranth: enhanced single-cell transcript assembly via discriminative modelling of UMI reads and internal reads.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has transformed transcriptome profiling at cellular resolution, yet accurate reconstruction of full-length transcripts for individual cells remains a central challenge. Emerging scRNA-seq protocols can produce reads that span entire transcripts, enabling isoform-level expression analysis. For example, Smart-seq protocols combine unique molecular identifier (UMI)-linked reads that index and stitch together multiple reads from the same molecule, with internal reads filling coverage gaps. We demonstrate that these read types exhibit markedly different biological and statistical properties in strandness, 5'/3' coverage bias, and genomic locality. Existing assemblers fail to leverage these distinctions, yielding suboptimal assembly. RESULTS: We developed Amaranth, a novel single-cell assembler that discriminatively models UMI and internal reads. Amaranth implements heuristics specifically designed to address the distinct biases of UMI-linked and internal reads, enabling accurate strandness assignment for internal reads, reliable splicing graph refinement, and precise transcript start site determination. We also developed Amaranth-meta, which integrates information across cells to enhance individual cell assemblies. Benchmarked on Smart-seq3 datasets from human HEK293T and mouse fibroblast cells, Amaranth outperformed other state-of-the-art assemblers in assembling individual cells and in meta-assembly. Amaranth advances isoform-level analysis in single-cell transcriptomics, facilitating detailed studies at cellular resolution. AVAILABILITY AND IMPLEMENTATION: Amaranth is implemented in C++ and is freely available at https://github.com/Shao-Group/amaranth under the BSD-3-Clause license. Scripts, documentation, and data for reproducing experiments in this manuscript are available at https://github.com/Shao-Group/amaranth-test.

Single-Cell Gene Expression Analysis↗

PubMed2026

Integrative analysis of single-cell sequencing identifies CD8+ TIM3+ CD101+ T cell-associated genes as prognostic biomarkers in breast cancer.

BACKGROUND: Breast cancer is a prevalent and deadly malignancy that significantly impacts women's quality of life and imposes financial burdens. Despite therapeutic advancements, tumour heterogeneity and frequent relapses remain major challenges. Accordingly, this study aimed to characterize immune features associated with CD8+ TIM3+ CD101+ T cells and develop a prognostic signature for breast cancer. METHODS: This study integrated single-cell and bulk transcriptomic datasets to characterize CD8+ TIM3+ CD101+ T cell (CCT)-related immune features and construct a prognostic signature in breast cancer. Single-cell RNA-seq data were sourced from the Gene Expression Omnibus (GEO) repository, and bulk transcriptomic data were from The Cancer Genome Atlas (TCGA) and GEO databases. Analytical methods included pseudo-time trajectory reconstruction (Monocle2), intercellular signalling analysis (CellChat), functional enrichment (ClusterProfiler), and immune profiling (ssGSEA). Prognostic modeling was conducted using least absolute shrinkage and selection operator (LASSO) Cox regression, with validation via Kaplan-Meier and time-dependent receiver operating characteristic (ROC) analyses. RESULTS: Single-cell analysis identified 17 clusters spanning seven cell types, including T cells, myeloid cells, and epithelial cells. T-cell sub-clustering revealed four subtypes. Pseudotime analysis suggested a potential state-transition relationship between CD8+ CD101- TIM3+ and CD8+ CD101+ TIM3+ T-cell states. A total of 121 differentially expressed genes were enriched in vital biological processes. An 11-gene prognostic model showed strong predictive power across cohorts. Single-cell T-cell reclustering identified a CD8+ CD101+ TIM3+ T-cell subpopulation, which was primarily characterized by the expression of markers such as CD101 and HAVCR2/TIM3. CONCLUSIONS: This study maps cellular heterogeneity and molecular networks in breast cancer, offering insights for targeted therapy and improved prognosis.

Breast invasive carcinoma↗

PubMed1994

Mass-action view of single-cell responses to stimulation of the receptive field and/or beyond: exemplification with data from the rabbit primary visual cortex.

Whereas single cells in the visual cortex prefer moving light bars, mass-action responses are evoked better by diffuse luminance changes. This discrepancy was investigated by quantitatively comparing the response properties of individual cells with those of a representative group of cells. The latter responses were derived from the single-cell responses, which were obtained from recording in the rabbit. These quantitative estimates of mean responses resolve the discrepancy between the single-cell domain and the mass-action domain: from the single-cell point of view, a properly oriented moving-bar stimulus is much more effective than a diffuse-light stimulus. The corresponding mass-action response to one common moving-bar stimulus, however, is as small as the mean response to a diffuse-light stimulus (which may even be presented at retinotopically non-corresponding sites). The peak intensities of these mass responses are even much stronger with the diffuse-light stimuli. The same conclusions are valid for the cat, as could be verified from published data. The restrictions of the local receptive field concept that may be implied by the mass-action view of cortical activity and the potential functional relevance of mass activities are discussed.

Animals↗

PubMed1993

Cytokine production in mononuclear cells of human milk studied at the single-cell level.

In this study, we demonstrate that mononuclear cells of human milk have a potential for production of many different cytokines. We applied a technique for cytokine detection at the single-cell level using cytokine specific MAb and immunofluorescence. The characteristic staining pattern obtained represents intracellular cytokine production, which allows for the assessment of the cellular origin of production. Milk mononuclear cells were mitogen-stimulated in vitro and cultured for 4 h and then stained for 13 cytokines. Lipopolysaccharide stimulation induced extensive production of the following monokines: IL-1 alpha, IL-1 beta, IL-1ra, IL-6, IL-8, and tumor necrosis factor-alpha. IL-10 and granulocyte-macrophage colony-stimulating factor were smaller products, although detectable in most samples. The abundant monokine production correlated with the high number of macrophages in milk. Spontaneous monokine production in unstimulated cells could be detected in six out of 11 samples. The highest incidence was evident for IL-8. No spontaneous lymphokine production was detected. Considering the low proportion of lymphocytes, stimulation with phorbol myristate acetate in combination with ionomycin resulted in considerable production of the following lymphokines: IL-2, IL-3, IL-4, IL-10, interferon-gamma, tumor necrosis factor-alpha. Macrophages contributed to the high production of tumor necrosis factor-alpha and GM-CSF. IL-5 synthesis was detectable in only one sample. This work reveals that human milk mononuclear cells are potent producers of cytokines when mitogen stimulated in vitro. The in vivo implications of these findings remain to be investigated further.

Antibodies, Monoclonal↗

PubMed2026

scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.

BACKGROUND: Existing virtual perturbation methods can often infer directional changes by comparing predicted post-perturbation expression profiles with control cells. However, workflows that directly return direction-specific downstream candidate genes together with confidence scores, evidence support and interpretable summaries remain limited. We developed scGPA, an LLM-assisted workflow system for directional single-cell virtual gene perturbation analysis. METHODS: scGPA starts from raw single-cell RNA sequencing data and performs quality control, normalization, dimensionality reduction, clustering and cell-group selection. It then constructs cell-group-specific wild-type regulatory networks using repeated subsampling, principal component regression (PCR)/Ridge-based network inference and CP tensor denoising. Based on these networks, scGPA simulates dose-aware virtual knockdown of the target gene and applies signed perturbation propagation to estimate the magnitude and direction of downstream transcriptional responses. LLM assistance is used for marker-based cell-type annotation, evidence-guided candidate prioritization and user-facing biological summarization. RESULTS: We benchmarked scGPA across five public Perturb-seq datasets and compared its performance with GEARS, scGPT and a random baseline. The overall correct prediction rate of scGPA was 23.0%, exceeding those of GEARS (20.7%), scGPT (15.1%) and the random baseline (13.6%). These results indicate that scGPA achieved a higher correct prediction rate than the two comparator models and the random baseline. We subsequently evaluated scGPA using a public osteosarcoma single-cell dataset and performed qRT-PCR validation in 143B osteosarcoma cells. Among genes with significant experimental changes, scGPA achieved a directional concordance of 76.9%. When all tested downstream genes were counted, 37.0% were directionally correct, 51.9% showed no significant change and 11.1% changed in the opposite direction. CONCLUSIONS: scGPA provides a practical workflow system for predicting and prioritizing direction-specific downstream transcriptional responses after target-gene perturbation. By integrating single-cell regulatory network inference, signed virtual perturbation and LLM-assisted interpretation, scGPA supports target-gene function inference and downstream mechanistic investigation from single-cell transcriptomic data.

Single-Cell Gene Expression Analysis↗

PubMed2002

Nanosensor for in vivo measurement of the carcinogen benzo[a]pyrene in a single cell.

This work describes the fabrication and the application of an antibody-based fiber-optic nanosensor for in situ measurements of the carcinogen benzo[a]pyrene (BaP) in a single cell. This antibody-based spectroscopic nanosensor is miniaturized enabling the detection of fluorescent analytes in single cells. In addition to measuring fluorescent analytes in single cells, the nanosensor has the potential to be applied for both diagnostic and proteomics purposes. In this work, the human breast carcinoma cell line, MCF-7, was used as the model system to perform BaP measurements in single cells. A standard concentration curve for BaP was established and used to perform quantitative analyses of BaP in individual cells. From these analyses, it was estimated that the concentration of BaP in the individual cells investigated was approximately 3.61 x 10(-10) M. The results obtained demonstrate the application of antibody-based nanosensors for performing in situ measurements inside a single cell.

Antibodies↗

PubMed2005

Cell cycle analysis of Taxus suspension cultures at the single cell level as an indicator of culture heterogeneity.

Single cell growth and division was measured via flow cytometry in order to characterize the metabolic variability of Taxus cuspidata suspension cultures, which produce the valuable secondary metabolite Taxol. Good agreement was observed between the cell cycle distribution and biomass accumulation over the batch culture period. Specific growth rates of 0.13 days(-1) by fresh weight and 0.15 days(-1) by dry weight were measured. Elicitation with methyl jasmonate (MJ) significantly decreased both cell cycle progression and biomass accumulation, as the specific growth rate decreased to 0.027 days(-1) by fresh and dry weight. Despite the decrease in biomass accumulation for MJ elicited cultures, sucrose utilization was not significantly different from control cultures. MJ elicitation also increased the accumulation of paclitaxel and other taxanes. The accumulation of upstream taxanes (baccatin III and 10-deactylbaccatin III) increased during exponential growth, reached a maximum around day 12, and then declined throughout the stationary phase. The paclitaxel concentration increased during both exponential growth and stationary phase, reaching a maximum around days 20-25. Throughout the culture period, greater than 70% of the cells were in G(0)/G(1) phase of the cell cycle. Studies using bromodeoxyuridine (BrdU) incorporation showed that approximately 65% of the Taxus cells are noncycling, even during exponential growth. Although the role of these cells is currently unknown, the presence of a large, noncycling subpopulation can have a significant impact on the utilization of plant cell culture technology for the large-scale production of paclitaxel. These results demonstrate that there is a high degree of metabolic heterogeneity in Taxus cuspidata suspension cultures. Understanding this heterogeneity is important for the optimization of plant cell cultures, particularly the reduction of production variability.

Bromodeoxyuridine↗

PubMed2004

A single cell electroporation chip.

Increasing the cell membrane's permeability can be accomplished via single cell electroporation. Polar substances that cannot otherwise permeate the plasma membrane (such as dyes, drugs, DNA, proteins, peptides, and amino acids) can thus be introduced into the cell. We developed a polymeric chip that can selectively immobilize and locally electroporate single cells. This easy-to-use chip focuses the electric field, eliminating the need to manipulate electrodes or glass pipettes. Moreover, this device allows parallel single cell electroporation. We demonstrate the effectiveness of our device design by electroporating HeLa cells using low applied voltages (< 1 V). We found the average transmembrane potential required for electroporation of HeLa cells to be 0.51 +/- 0.13 V. Membrane permeation is assessed electrically by measuring characteristic 'jumps' in current that correspond to drops in cell resistance, and microscopically by recording either the escape of cytoplasmic dye Calcein AM or the entrance of Trypan blue stain.

Cell Membrane Permeability↗

PubMedDate not supplied

Microfluidics device for single cell gene expression analysis in Saccharomyces cerevisiae.

We have measured single-cell gene expression over time using a microfluidics-based flow cell which physically traps individual yeast using microm-sized structures (yeast jails). Our goal was to determine variability of gene expression within a cell over time, as well as variability between individual cells. In our flow cell system, yeast jails are fabricated out of PDMS and gene expression is visualized using fluorescently-tagged proteins of interest. Previously, single-cell yeast work has been done using micromanipulation on agar, or FACS. In the present device agar is eliminated, resulting in a superior optical system. The flow of media through the flow cell washes daughter cells away, eliminating the need for micromanipulation. Unlike FACS, the described device can track individual yeast over a time course of many hours. The flow cells are compatible with the needs of quantitative fluorescence microscopy, and allow simultaneous measurements to be done on a large number of individual yeast. We used these flow cells to determine the expression of HSP104-GFPand RAS2-YFP, genes known to affect yeast life span. The results demonstrate inter-cell variation in expression of both genes that could not have been detected without this single-cell analysis.

Bacterial Proteins↗

PubMed1999

Detection of aneuploidy in single cells using comparative genomic hybridization.

The ability of comparative genomic hybridization (CGH) to detect aneuploidy following universal amplification of DNA from a single cell, or a small number of cells, was investigated with a view to preimplantation diagnosis following in vitro fertilization, and prenatal diagnosis using fetal erythroblasts obtained from maternal blood. The DNA obtained from lysed single cells was amplified using degenerate oligonucleotide-primed PCR (DOP-PCR). This product was labelled using nick translation and hybridized together with normal reference genomic DNA. The CGH fluorescent ratio profiles obtained could be used to determine aneuploidy with cut-off thresholds of 0.75 and 1.25. Deviation in the profiles in the heterochromatic regions was reduced by using, as a reference sample, normal genomic DNA that had also undergone DOP-PCR. Single cells known to be trisomic for chromosomes 13, 18 or 21 were analysed using this technique. The resolution of CGH with amplified DNA from a single cell is of the order of 40 Mb, sufficient for the diagnosis of trisomy 21, and possibly segmental aneuploidy of equivalent size. These results, and those of others, demonstrate that diagnosis of chromosomal aneuploidy in single cells is possible using CGH with DOP-PCR amplified DNA.

Aneuploidy↗

PubMed2005

Micro/Nanofluidic device for single-cell-based assay.

In this paper we have proposed and demonstrated a microfluidic device for a fast and parallel single-cell based assay. The proposed device is designed to passively capture single-cells or beads on multiple cell-positioning sites by a pre-defined fluidic stream and inject specific reagents or drugs onto each isolated single-cell. The device consists of surface-modified silicon channels capped with a grooved polydimethylsiloxane (PDMS) cover layer. A cell capture experiment has been performed using polystyrene beads as well as CHO DG44 living cells, and successful positioning of a single-cell on the cell-positioning sites was achieved. Also, we have demonstrated independent drug injection into a specific target cell.

Animals↗