Single-cell Analysis Techniques Research Articles

Monitoring of Bacillus spore-forming dynamics through flow cytometry.

The plate counting method is a traditional and widely accepted technique for live cell counting, often employed for Bacillus enumeration and spore forming rate calculations. However, this method requires at least 12 h to generate results, making it unsuitable for real-time monitoring of bacterial growth status and spore transformation rate. Bacillus thuringiensis crystals, produced during sporulation, are widely used as microbial pesticides, with high demand for industrial scale production. Variations in cultivation conditions and harvest timing during large-scale pore production of Bacillus thuringiensis significantly affect spore forming rate, impacting crystallization yield. Nevertheless, there is a lack of real-time monitoring methods for spore conversion rate. Flow cytometry (FCM), a well-established technique for single-cell analysis in eukaryotic cells, has been successfully applied in bacterial detection in environmental and food samples. In this study, we introduced a rapid flow cytometry-based method for determining spore forming rate of Bacillus thuringiensis, with two nucleic acid dyes, SYTO24 and LDS751. The method enables dynamic monitoring of spore, vegetative cell, and viable but non-culturable/dead cell proportions during the whole cultivation process, and spore forming rate could be gained within 30 min. Data of spore forming rate by FCM method is consistent with that by plate counting method, offering a faster and more efficient approach for assessing sporulation status in industrial Bacillus thuringiensis microbial pesticide production.

Starch granules in algal cells play an inherent role to shape the popular SSC signal in flow cytometry

ObjectiveFlow cytometry (FC) is probably the most important technique for single-cell analysis. It’s precisely, rapid, and suitable for multidimensional single-cell analysis. The commonly used side scatter (SSC) intensity determined by FC is often interpreted as a measure of the internal cellular complexity of cells. In simple terms, the more structured a cell is, the higher the SSC intensity quantified by FC. Nevertheless, most of the studies that support this interpretation are based on data derived from animal or human cell lines and while it is assumed that the results can also be transferred to plant or algal cell lines, the details remain unclear. The objective of the recent work is to clarify the interpretation of the SSC signal from algal cells.ResultsAlgal lipid droplets and their starch play an inherent role to shape the popular SSC signal derived from FC. This was shown by a theoretical approach based on Lorenz–Mie theory. These results were supported by experiments with different model cultures of Chlamydomonas reinhardtii in which a high linear correlation was observed between the SSC signal and the ‘physical’ starch quantity.

Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes—EP300, MTOR, NFE2L1, TXN—exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

A cross-reactive imaging matrix of membrane protein profiling for single-cell analysis

Membrane proteins play a critical role in cellular processes such as chemical transport, signal transduction, and enzymatic catalysis. A comprehensive understanding of membrane protein types and accurately measuring their expression levels are essential for advancing cell biology. However, current methods often fail to characterize the types and amounts of multiple membrane proteins in specific cell types due to inherent measurement limitations. Here, we have developed a single-cell image analysis technique based on a cross-reactive imaging matrix using membrane protein tags. This technique allows quantitative and qualitative analysis of multiple membrane protein types and their expression levels within single cells. As a proof of concept, we used four specific DNA aptamers to tag four different membrane proteins in seven different cell types. The four proteins are MUC1, EGFR, HeR2, and TLR4. The seven cell lines are 3t3, 4T1, A549, CT26, HeLa, Hepa1–6, Raw, respectively. We captured a comprehensive image matrix using wide-field fluorescence imaging, allowing precise quantification of protein expression at the single-cell level. Our approach introduces a multi-labeling analysis method for membrane proteins and reveals the influence of membrane protein diversity on cell heterogeneity analysis. We found that the accuracy of cell heterogeneity recognition can be affected by adjusting the number of membrane proteins. As the diversity of membrane protein species increases, the accuracy of cell type recognition increases. Our approach facilitates the evaluation of heterogeneity in diverse cells, providing new insights into cellular diversity and a powerful tool for studying single-cell biology.

Microfluidic-Based Electrical Operation and Measurement Methods in Single-Cell Analysis.

Cellular heterogeneity plays a significant role in understanding biological processes, such as cell cycle and disease progression. Microfluidics has emerged as a versatile tool for manipulating single cells and analyzing their heterogeneity with the merits of precise fluid control, small sample consumption, easy integration, and high throughput. Specifically, integrating microfluidics with electrical techniques provides a rapid, label-free, and non-invasive way to investigate cellular heterogeneity at the single-cell level. Here, we review the recent development of microfluidic-based electrical strategies for single-cell manipulation and analysis, including dielectrophoresis- and electroporation-based single-cell manipulation, impedance- and AC electrokinetic-based methods, and electrochemical-based single-cell detection methods. Finally, the challenges and future perspectives of the microfluidic-based electrical techniques for single-cell analysis are proposed.

Single-Cell Analysis of Bone-Marrow-Disseminated Tumour Cells

Metastasis frequently targets bones, where cancer cells from the primary tumour migrate to the bone marrow, initiating new tumour growth. Not only is bone the most common site for metastasis, but it also often marks the first site of metastatic recurrence. Despite causing over 90% of cancer-related deaths, effective treatments for bone metastasis are lacking, with current approaches mainly focusing on palliative care. Circulating tumour cells (CTCs) are pivotal in metastasis, originating from primary tumours and circulating in the bloodstream. They facilitate metastasis through molecular interactions with the bone marrow environment, involving direct cell-to-cell contacts and signalling molecules. CTCs infiltrate the bone marrow, transforming into disseminated tumour cells (DTCs). While some DTCs remain dormant, others become activated, leading to metastatic growth. The presence of DTCs in the bone marrow strongly correlates with future bone and visceral metastases. Research on CTCs in peripheral blood has shed light on their release mechanisms, yet investigations into bone marrow DTCs have been limited. Challenges include the invasiveness of bone marrow aspiration and the rarity of DTCs, complicating their isolation. However, advancements in single-cell analysis have facilitated insights into these elusive cells. This review will summarize recent advancements in understanding bone marrow DTCs using single-cell analysis techniques.

Concurrent analysis of genome and transcriptome in one single cell

Thus far, multiple techniques for single cell analysis have been developed, yet we lack a relatively simple tool to assess DNA and RNA from the same cell at whole-transcriptome and whole-genome depths. Here we present an updated method for physical separation of cytoplasmic RNA from the nuclei, which allows for simultaneous studies of DNA and RNA from the same single cell. The method consists of three steps—(1) immobilization of a single cell on solid substrate, (2) hypotonic lysis of immobilized single cell, and (3) separation of cytosol containing aqueous phase and immobilized nucleus. We found that DNA and RNA extracted from single cell using our approach is suitable for downstream sequencing-based applications. We demonstrated that the coverage of transcriptome and genome sequencing data obtained after DNA/RNA separation is similar to that observed without separation. We also showed that the separation procedure does not create any noticeable bias in observed mutational load or mutation spectra. Thus, our method can serve as a tool for simultaneous complex analysis of the genome and transcriptome, providing necessary information on the relationship between somatic mutations and the regulation of gene expression.

Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research.

Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.

Testing a Nanoparticle Reagent for Imaging Mass Cytometry.

Mass cytometry (MC), a powerful single-cell analysis technique, has limitations in detecting low-abundance biomarkers. Nanoparticle (NP) reagents offer the potential for enhancing sensitivity by carrying large numbers of heavy metal isotopes. Here, we report NP reporters for imaging mass cytometry (IMC) based on NaYF4:Yb3+/Er3+ NPs. A two-step ligand exchange was used to coat NP surfaces with either methoxy-PEG2K-neridronate (PEG-Ner) and/or poly(sulfobetaine methacrylate)-neridronate (PSBMA-Ner). Both modifications provided long-term colloidal stability in PBS buffer. IMC measurements on tonsil tissue showed that PSBMA-Ner or a 1:1 mixture of PSBMA-Ner + PEG-Ner effectively suppressed nonspecific binding (NSB) at 2 × 1010 NPs/mL, unlike PEG-Ner alone. However, breast cancer tissue samples showed increased NSB at titers above 2 × 1010 NPs/mL. Reduced NSB with mixed PEG-Ner and PSBMA-Ner coatings opens the door for using heterobifunctional PEGs for the development of NP conjugates with bioaffinity agents, enabling more sensitive and specific MC analyses.

Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types.

Congenital heart disease (CHD) represents a spectrum of inborn heart defects influenced by genetic and environmental factors. This study advances the field by analyzing gene expression profiles in 21,034 cardiac fibroblasts, 73,296 cardiomyocytes, and 35,673 endothelial cells, utilizing single-cell level analysis and machine learning techniques. Six CHD conditions: dilated cardiomyopathy (DCM), donor hearts (used as healthy controls), hypertrophic cardiomyopathy (HCM), heart failure with hypoplastic left heart syndrome (HF_HLHS), Neonatal Hypoplastic Left Heart Syndrome (Neo_HLHS), and Tetralogy of Fallot (TOF), were investigated for each cardiac cell type. Each cell sample was represented by 29,266 gene features. These features were first analyzed by six feature-ranking algorithms, resulting in several feature lists. Then, these lists were fed into incremental feature selection, containing two classification algorithms, to extract essential gene features and classification rules and build efficient classifiers. The identified essential genes can be potential CHD markers in different cardiac cell types. For instance, the LASSO identified key genes specific to various heart cell types in CHD subtypes. FOXO3 was found to be up-regulated in cardiac fibroblasts for both Dilated and hypertrophic cardiomyopathy. In cardiomyocytes, distinct genes such as TMTC1, ART3, ARHGAP24, SHROOM3, and XIST were linked to dilated cardiomyopathy, Neo-Hypoplastic Left Heart Syndrome, hypertrophic cardiomyopathy, HF-Hypoplastic Left Heart Syndrome, and Tetralogy of Fallot, respectively. Endothelial cell analysis further revealed COL25A1, NFIB, and KLF7 as significant genes for dilated cardiomyopathy, hypertrophic cardiomyopathy, and Tetralogy of Fallot. LightGBM, Catboost, MCFS, RF, and XGBoost further delineated key genes for specific CHD subtypes, demonstrating the efficacy of machine learning in identifying CHD-specific genes. Additionally, this study developed quantitative rules for representing the gene expression patterns related to CHDs. This research underscores the potential of machine learning in unraveling the molecular complexities of CHD and establishes a foundation for future mechanism-based studies.

Rapid Screening of Biomarkers in KYSE-150 Cells Exposed to Polycyclic Aromatic Hydrocarbons via Inkjet Printing Single-Cell Mass Spectrometry.

Single-cell analysis by mass spectrometry (MS) is emerging as a powerful tool that not only contributes to cellular heterogeneity but also offers an unprecedented opportunity to predict pathology onset and facilitates novel biomarker discovery. However, the development of single-cell MS analysis techniques with a focus on sample extraction, separation, and ionization methods for volume-limited samples and complexity of cellular samples are still a big challenge. In this study, we present a high-throughput approach to inkjet drop on demand printing single-cell MS for rapid screening of biomarkers of polycyclic aromatic hydrocarbon (PAH) exposure at the KYSE-150 cell, aiming to elucidate the pathogenesis of PAH-induced esophageal cancer. With an analytical bulk KYSE-150 cell throughput of up to 51 cells per minute, the method provides a new opportunity for simultaneous single-cell analysis of multiple biomarkers. We screened 930 characteristic ions from 3,683 detected peak signals and identified 91 distinctive molecules that exhibited significant differences under various concentrations of PAH exposure. These molecules have potential as clinical diagnostic biomarkers. Additionally, the current study identifies specific biomarkers that behave completely opposite in single-cell and multicell lipidomics as the concentration of PAH changes. These biomarkers potentially subdivide KYSE-150 cells into PAH-sensitive and PAH-insensitive types, providing a basis for revealing PAH toxicity and disease pathogenesis from the heterogeneity of cellular metabolism.

Markers of Dermal Fibroblast Subpopulations for Viable Cell Isolation via Cell Sorting: A Comprehensive Review.

Fibroblasts are among the most abundant cell types in the human body, playing crucial roles in numerous physiological processes, including the structural maintenance of the dermis, production of extracellular matrix components, and mediation of inflammatory responses. Despite their importance, fibroblasts remain one of the least characterized cell populations. The advent of single-cell analysis techniques, particularly single-cell RNA sequencing (scRNA-seq) and fluorescence-activated cell sorting (FACS), has enabled detailed investigations into fibroblast biology. In this study, we present an extensive analysis of fibroblast surface markers suitable for cell sorting and subsequent functional studies. We reviewed over three thousand research articles describing fibroblast populations and their markers, characterizing and comparing subtypes based on their surface markers, as well as their intra- and extracellular proteins. Our detailed analysis identified a variety of distinct fibroblast subpopulations, each with unique markers, characteristics dependent on their location, and the physiological or pathophysiological environment. These findings underscore the diversity of fibroblasts as a cellular population and could lead to the development of novel diagnostic and therapeutic tools.

Rapid Microbial Profiling through Multimodal Biosensors for Transversal Analysis.

The intricate interactions between host and microbial communities hold significant implications for biology and medicine. However, traditional microbial profiling methods face limitations in processing time, measurement of absolute abundance, detection of low biomass, discrimination between live and dead cells, and functional analysis. This study introduces a rapid multimodal microbial characterization platform, Multimodal Biosensors for Transversal Analysis (MBioTA), for capturing the taxonomy, viability, and functional genes of the microbiota. The platform incorporates single cell biosensors, scalable microwell arrays, and automated image processing for rapid transversal analysis in as few as 2 h. The multimodal biosensors simultaneously characterize the taxon, viability, and functional gene expression of individual cells. By automating the image processing workflow, the single cell analysis techniques enable the quantification of bacteria with sensitivity down to 0.0075%, showcasing its capability in detecting low biomass samples. We illustrate the applicability of the MBioTA platform through the transversal analysis of the gut microbiota composition, viability, and functionality in a familial Alzheimer's disease mouse model. The effectiveness, rapid turnaround, and scalability of the MBioTA platform will facilitate its application from basic research to clinical diagnostics, potentially revolutionizing our understanding and management of diseases associated with microbe-host interactions.

Phenotypic Landscape of Immune Cells in Sepsis: Insights from High-Dimensional Mass Cytometry.

Understanding the sepsis-induced immunological response can be facilitated by identifying phenotypic changes in immune cells at the single-cell level. Mass cytometry, a novel multiparametric single-cell analysis technique, offers considerable benefits in characterizing sepsis-induced phenotypic changes in peripheral blood mononuclear cells. Here, we analyzed peripheral blood mononuclear cells from 20 sepsis patients and 10 healthy donors using mass cytometry and employing 23 markers. Both manual gating and automated clustering approaches (PhenoGraph) were used for cell identification, complemented by uniform manifold approximation and projection (UMAP) for dimensionality reduction and visualization. Our study revealed that patients with sepsis exhibited a unique immune cell profile, marked by an increased presence of monocytes, B cells, and dendritic cells, alongside a reduction in natural killer (NK) cells and CD4/CD8 T cells. Notably, significant changes in the distributions of monocytes and B and CD4 T cells were observed. Clustering with PhenoGraph unveiled the subsets of each cell type and identified elevated CCR6 expression in sepsis patients' monocyte subset (PG#5), while further PhenoGraph clustering on manually gated T and B cells discovered sepsis-specific CD4 T cell subsets (CCR4low CD20low CD38low) and B cell subsets (HLA-DRlow CCR7low CCR6high), which could potentially serve as novel diagnostic markers for sepsis.

Preparation empty peptide-receptive MHC class I complex for large-scale detection through photolabile peptide ligands

Peptide-major histocompatibility complex (pMHC) multimers are wide recognized as the premier technique for detecting, characterizing, and isolating antigen-specific CD8+ T-cell subsets. These multimers are specifically useful in studying infections, autoimmune conditions, and cancer through single-cell analysis techniques such as flow cytometry and fluorescence microscopy. However, the development of high-throughput assays with commercially available pMHC tetramers can be expensive, while in-house production may pose challenges for most biology research laboratories. In this context, we introduce a cost-friendly and uncomplicated protocol to prepare empty MHC class I tetramers using disulfide-stabilized molecules and photolabile peptide ligands. Our method relies on disulfide bond-stabilized MHC-I molecules, which demonstrated stability when folded into stable monomers in the presence of a photolabile epitope. These monomers, upon ultraviolet irradiation and streptavidin binding, efficiently assemble into tetramers devoid of any peptide. Following a short incubation with the peptide of interest under gentle conditions, the resulting pMHC tetramer effectively detects patient-sourced, neoantigen-specific T cells. Our unique approach streamlines large-scale pMHC generation, thus paving the way for advancements in T cell-based diagnostics and personalized therapies.

Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature.

We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma insitu (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.

Single-cell sequencing advances in research on mesenchymal stem/stromal cells.

Mesenchymal stem/stromal cells (MSCs), originating from the mesoderm, represent a multifunctional stem cell population capable of differentiating into diverse cell types and exhibiting a wide range of biological functions. Despite more than half a century of research, MSCs continue to be among the most extensively studied cell types in clinical research projects globally. However, their significant heterogeneity and phenotypic instability have significantly hindered their exploration and application. Single-cell sequencing technology emerges as a powerful tool to address these challenges, offering precise dissection of complex cellular samples. It uncovers the genetic structure and gene expression status of individual contained cells on a massive scale and reveals the heterogeneity among these cells. It links the molecular characteristics of MSCs with their clinical applications, contributing to the advancement of regenerative medicine. With the development and cost reduction of single-cell analysis techniques, sequencing technology is now widely applied in fundamental research and clinical trials. This study aimed to review the application of single-cell sequencing in MSC research and assess its prospects.

Optimizing Microfluidic Impedance Cytometry by Bypass Electrode Layout Design.

Microfluidic impedance cytometry (MIC) has emerged as a popular technique for single-cell analysis. Traditional MIC electrode designs consist of a pair of (or three) working electrodes, and their detection performance needs further improvements for microorganisms. In this study, we designed an 8-electrode MIC device in which the center pair was defined as the working electrode, and the connection status of bypass electrodes could be changed. This allowed us to compare the performance of layouts with no bypasses and those with floating or grounding electrodes by simulation and experiment. The results of detecting Φ 5 μm beads revealed that both the grounding and the floating electrode outperformed the no bypass electrode, and the grounding electrode demonstrated the best signal-to-noise ratio (SNR), coefficient of variation (CV), and detection sensitivity. Furthermore, the effects of different bypass grounding areas (numbers of grounding electrodes) were investigated. Finally, particles passing at high horizontal positions can be detected, and Φ 1 μm beads can be measured in a wide channel (150 μm) using a fully grounding electrode, with the sensitivity of bead volume detection reaching 0.00097%. This provides a general MIC electrode optimization technology for detecting smaller particles, even macromolecular proteins, viruses, and exosomes in the future.

Single-cell sequencing analysis and multiple machine-learning models revealed the cellular crosstalk of dendritic cells and identified FABP5 and KLRB1 as novel biomarkers for psoriasis.

Psoriasis is an immune-mediated disorder influenced by environmental factors on a genetic basis. Despite advancements, challenges persist, including the diminishing efficacy of biologics and small-molecule targeted agents, alongside managing recurrence and psoriasis-related comorbidities. Unraveling the underlying pathogenesis and identifying valuable biomarkers remain pivotal for diagnosing and treating psoriasis. We employed a series of bioinformatics (including single-cell sequencing data analysis and machine learning techniques) and statistical methods to integrate and analyze multi-level data. We observed the cellular changes in psoriatic skin tissues, screened the key genes Fatty acid binding protein 5 (FABP5) and The killer cell lectin-like receptor B1 (KLRB1), evaluated the efficacy of six widely prescribed drugs on psoriasis treatment in modulating the dendritic cell-associated pathway, and assessed their overall efficacy. Finally, RT-qPCR, immunohistochemistry, and immunofluorescence assays were used to validate. The regulatory influence of dendritic cells (DCs) on T cells through the CD70/CD27 signaling pathway may emerge as a significant facet of the inflammatory response in psoriasis. Notably, FABP5 and KLRB1 exhibited up-regulation and co-localization in psoriatic skin tissues and M5-induced HaCaT cells, serving as potential biomarkers influencing psoriasis development. Our study analyzed the impact of DC-T cell crosstalk in psoriasis, elucidated the characterization of two biomarkers, FABP5 and KLRB1, in psoriasis, and highlighted the promise and value of tofacitinib in psoriasis therapy targeting DCs.

Multiplexed fluorescence and scatter detection with single cell resolution using on-chip fiber optics for droplet microfluidic applications

Droplet microfluidics has emerged as a critical component of several high-throughput single-cell analysis techniques in biomedical research and diagnostics. Despite significant progress in the development of individual assays, multiparametric optical sensing of droplets and their encapsulated contents has been challenging. The current approaches, most commonly involving microscopy-based high-speed imaging of droplets, are technically complex and require expensive instrumentation, limiting their widespread adoption. To address these limitations, we developed the OptiDrop platform; this platform is a novel optofluidic setup that leverages the principles of flow cytometry. Our platform enables on-chip detection of the scatter and multiple fluorescence signals from the microfluidic droplets and their contents using optical fibers. The highly customizable on-chip optical fiber-based signal detection system enables simplified, miniaturized, low-cost, multiparametric sensing of optical signals with high sensitivity and single-cell resolution within each droplet. To demonstrate the ability of the OptiDrop platform, we conducted a differential expression analysis of the major histocompatibility complex (MHC) protein in response to IFNγ stimulation. Our results showed the platform’s ability to sensitively detect cell surface biomarkers using fluorescently labeled antibodies. Thus, the OptiDrop platform combines the versatility of flow cytometry with the power of droplet microfluidics to provide wide-ranging, scalable optical sensing solutions for research and diagnostics.