High-throughput Data Research Articles

A signal-diffusion-based unsupervised contrastive representation learning for spatial transcriptomics analysis.

Spatial transcriptomics allows for the measurement of high-throughput gene expression data while preserving the spatial structure of tissues and histological images. Integrating gene expression, spatial information, and image data to learn discriminative low-dimensional representations is critical for dissecting tissue heterogeneity and analyzing biological functions. However, most existing methods have limitations in effectively utilizing spatial information and high-resolution histological images. We propose a signal-diffusion-based unsupervised contrast learning method (SDUCL) for learning low-dimensional latent embeddings of cells/spots. SDUCL integrates image features, spatial relationships and gene expression information. We designed a signal diffusion microenvironment discovery algorithm, which effectively captures and integrates interaction information within the cellular microenvironment by simulating the biological signal diffusion process. By maximizing the mutual information between the local representation and the microenvironment representation of cells/spots, SDUCL learns more discriminative representations. SDUCL was employed to analyze spatial transcriptomics datasets from multiple species, encompassing both normal and tumor tissues. SDUCL performed well in downstream tasks such as clustering, visualization, trajectory inference, and differential gene analysis, thereby enhancing our understanding of tissue structure and tumor microenvironments. https://github.com/WeiMin-Li-visual/SDUCL. Supplementary data are available at Bioinformatics online.

Harnessing genomic technologies for one health solutions in the tropics.

The targeted application of cutting-edge high-throughput molecular data technologies provides an enormous opportunity to address key health, economic and environmental issues in the tropics within the One Health framework. The Earth's tropical regions are projected to contain > 50% of the world's population by 2050 coupled with 80% of its biodiversity however these regions are relatively less developed economically, with agricultural productivity substantially lower than temperate zones, a large percentage of its population having limited health care options and much of its biodiversity understudied and undescribed. The generation of high-throughput molecular data and bespoke bioinformatics capability to address these unique challenges offers an enormous opportunity for people living in the tropics. MAIN: In this review we discuss in depth solutions to challenges to populations living in tropical zones across three critical One Health areas: human health, biodiversity and food production. This review will examine how some of the challenges in the tropics can be addressed through the targeted application of advanced omics and bioinformatics and will discuss how local populations can embrace these technologies through strategic outreach and education ensuring the benefits of the One Health approach is fully realised through local engagement. Within the context of the One Health framework, we will demonstrate how genomic technologies can be utilised to improve the overall quality of life for half the world's population.

MIMO and PDM-based intersatellite optical link for high-speed data transfer and remote sensing application.

Inter-Satellite Optical Wireless Communication (Is-OWC) is a pivotal technology for advancing global connectivity and the effectiveness of space-based operations. It serves as the linchpin for various applications such as global internet coverage, Earth observation, and remote sensing, bolstering our capacity to monitor the planet and deliver essential services. This paper presents the design and performance evaluation of a Polarization Division Multiplexing (PDM) and Multiple Input Multiple Output (MIMO)-based Is-OWC system operating at a data rate of 60 Gbps. The system was tested under varying transmission distances, atmospheric turbulence, and pointing error conditions. At a transmission distance of 10,000 km, the system achieved a Bit Error Rate (BER) of 6.76 × 10⁻3 for Channel 1 (X polarization) and 7.1 × 10⁻3 for Channel 4 (Y polarization), both within Forward Error Correction (FEC) limits. The introduction of a 4 × 4 MIMO configuration extended the transmission range to 14,000 km, with a corresponding BER of 9.1 × 10⁻3 for Channel 1 and 8.7 × 10⁻⁵ for Channel 4. Eye diagrams confirm successful signal reception, demonstrating that the system can maintain high data rates and low error rates over long distances. The proposed PDM-MIMO system showcases high-capacity, robust performance under challenging conditions, such as turbulence and pointing errors, validating its suitability for space-based communication applications. These findings highlight the potential of the system for future deployments in satellite networks, offering reliable, high-throughput, and low-latency data transmission over extended distances.

Identifying and validating the key regulatory transcription factor YY1 in the aging process of pancreatic beta cells based on bioinformatics.

The aging of pancreatic beta cells is closely associated with various diseases, such as impaired glucose tolerance, yet the underlying regulatory mechanisms remain unclear. In this study, we screened young and aged mouse pancreatic beta cells' high-throughput sequencing data from the GEO public database. Utilizing bioinformatics techniques, we identified the key regulatory factor YY1 in the aging process of pancreatic islets. We observed a significant decrease in the expression of YY1 in a D-gal-induced mouse model of pancreatic aging and an H2O2-induced MIN6 cell model of aging. Moreover, both vivo and vitro models, we found that the YY1 agonist eudesmin (EDN) improved glucose intolerance in mice, alleviated aging of pancreatic beta cells, and downregulated the expression of cell cycle protein P21. Mechanistically, we discovered that EDN inhibited the P38/JNK MAPK pathway in aging cells. In summary, our study confirms the regulatory role of the transcription factor YY1 in the aging process of pancreatic beta cells. This finding may provide a new approach for the clinical treatment of pancreatic aging-related diseases such as impaired glucose tolerance or diabetes.

Harnessing the Distributed Computing Paradigm for Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy allows fast and versatile elemental analysis, standing as a promising technique for a wide range of applications both at the research and industry levels. Yet, its high operation speed comes with a high throughput of data, which introduces some challenges at the level of the data processing domain, mainly due to the large computational load and data volume. In this work, we analyze and discuss opportunities of distributed computing paradigms and resources to address some of these challenges, covering most of the procedures usually employed in typical applications. We infer the possible impact of such computing resources by presenting some metrics of simple processing prototypes running in state-of-the-art computing facilities. Our results allow us to conclude that, while underexplored so far, these computing resources may allow for the development of tools for timely research and analysis in demanding applications and introduce novel solutions toward a more agile working environment.

ONIX: a unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior.

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge we developed ONIX, an open-source data acquisition system with high data throughput (2 GB s-1) and low closed-loop latencies (<1 ms) that uses a 0.3-mm thin tether to minimize behavioral impact. Head position and rotation are tracked in three dimensions and used to drive active commutation without torque measurements. ONIX can acquire data from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, three-dimensional trackers and other data sources. We performed uninterrupted, long (~7 h) neural recordings in mice as they traversed complex three-dimensional terrain, and multiday sleep-tracking recordings (~55 h). ONIX enabled exploration with similar mobility as nonimplanted animals, in contrast to conventional tethered systems, which have restricted movement. By combining long recordings with full mobility, our technology will enable progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL.

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for enhanced false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches and better sensitivity over popular differential abundance testing methods for datasets with smaller sample sizes. In a survival analysis of allogeneic hematopoietic cell transplant recipients, FLORAL demonstrated considerable improvement in microbial feature selection by utilizing longitudinal microbiome data over solely using baseline microbiome data.

Identification of Sequential Molecular Mechanisms and Key Biomarkers in Early Glaucoma by Integrated Bioinformatics Analysis.

Glaucoma is a neurodegenerative disease characterized by progressive optic nerve degeneration and retinal ganglion cell (RGC) loss. In early glaucoma, before obvious axon loss, highly organized pathological processes in RGCs occur sequentially, involving axons, dendrites and synaptic terminals. The optic nerve head (ONH) is the critical structure of early glaucomatous neurodegeneration. Taking advantage of high-throughput data from the ONH and the weighted gene coexpression network analysis (WGCNA) method, the current study aims to gain insight into the full scope of pathological events in early glaucoma and define their chronological sequence. The expression profiles of GSE26299, GSE110019, and GSE139605, which measure ONH gene expression in different glaucoma models, were downloaded from the Gene Expression Omnibus (GEO) database. In GSE26299, which uses 10.5-month-old DBA/2J mice, WGCNA was utilized to construct a gene coexpression network, and the most significant modules of early (NOE), moderate (MOD) and severe (SEV) glaucoma were identified. The differentially expressed genes (DEGs) of GSE110019 and GSE139605 significantly overlapped with the correlated module of the MOD group, so the 3 gene sets were analyzed together. Pathway enrichment analysis via the GO, KEGG, and Reactome pathways was subsequently performed, followed by protein‒protein interaction (PPI) analysis to screen key genes associated with each stage. Several hub gene expression patterns were identified in a glucocorticoid-induced glaucoma (GIG) model via quantitative PCR and immunostaining. The pink module was positively correlated with the NOE group (r = 0.48, p = 4e-04) and negatively correlated with the glaucoma stage (r = -0.88, p = 3e-17). The genes in the pink module were enriched in the synaptic transmission and axonal transport pathways. The tan module was negatively correlated with the NOE group (r = -0.43, p = 0.002) and positively correlated with the glaucoma stage (r = 0.77, p = 7e-11). The genes in the tan module were associated with pathways such as tight junctions, retinol metabolism, and linoleic acid metabolism. The purple module was positively correlated with the MOD group (r = 0.64, p = 5e-07). The common genes among the purple module and the DEGs of the two other datasets were enriched in pathways related to mitotic cell division, cytokine activity, and the extracellular matrix (ECM). The hub genes identified by PPI included Nrn1, Cplx1, Timp1, and Cdk1. Quantitative PCR and immunostaining confirmed that Limk1 expression was increased in the ONH of GIG mice. In early glaucomatous neuropathy, intrinsic changes in RGCs precede the activation of glial cells and ECM remodeling. These latter events are common pathological changes observed in the ONH in both cats and mice. Our study may provide new targets for the early detection and treatment of glaucoma.

Systematic profiling and analysis of growth and development responsive DE-lncRNAs in cluster bean (Cyamopsis tetragonoloba)

Long non-coding RNAs (lncRNAs) play crucial role in regulating genes involved in various processes including growth & development, flowering, and stress response in plants. The study aims to identify and characterize tissue-specific, growth & development and floral responsive differentially expressed lncRNAs (DE-lncRNAs) in cluster bean from a high-throughput RNA sequencing data. We have identified 3309 DE-lncRNAs, with an average length of 818 bp. Merely, around 4 % of DE-lncRNAs across the tissues were found to be conserved as rate of evolution of lncRNAs is high. Among the identified DE-lncRNAs, 204 were common in leaf vs. shoot, leaf vs. flower and flower vs. shoot. A total of 60 DE-lncRNAs targeted 10 protein-coding genes involved in flower development and initiation processes. We investigated 179 tissue-specific DE-lncRNAs based on tissue specificity index. Three DE-lncRNAs: Cb_lnc_0820, Cb_lnc_0430, Cb_lnc_0260 and their targeted genes shown their involvement in floral development and stress mechanisms, which were validated by Quantitative real-time PCR (qRT-PCR). The identified DE-lncRNAs were expressed higher in flower bud than in leaf and similar expression pattern was observed in both RNA-seq data and qRT-PCR analyses. Notably, 362 DE-lncRNAs were predicted as eTM-lncRNAs with the participation of 84 miRNAs. Whereas 46 DE-lncRNAs were predicted to possess the internal ribosomal entry sites (IRES) and can encode for small peptides. The regulatory networks established between DE-lncRNAs, mRNAs and miRNAs have provided an insight into their association with plant growth & development, flowering, and stress mechanisms. Comprehensively, the characterization of DE-lncRNAs in various tissues of cluster bean shed a light on interactions among lncRNAs, miRNAs and mRNAs and help understand their involvement in growth & development and floral initiation processes. The information retrieved from the analyses was shared in the public domain in the form of a database: Cb-DElncRNAdb, and made available at http://backlin.cabgrid.res.in/Cb-DElncRNA/index.php, which may be useful for the scientific community engaged in future breeding programs of cluster bean.

Metagenomic Detection of Multiple Viruses in Monk Parakeet (Myiopsitta monachus) in Australia.

Birds are known to harbour many pathogens, including circovirus, herpesviruses, adenoviruses and Chlamydia psittaci. Some of these pose zoonotic risks, while others, such as beak and feather disease virus (BFDV), have a significant impact on the conservation of endangered bird species. This study was aimed to determine the faecal virome of a group of apparently healthy Monk parakeet using high-throughput sequencing. Fresh faecal samples were collected from four Monk parakeets at a pet shop in Melbourne, Australia. Virus enrichment and nucleic acid extraction were performed on the faecal samples, followed by high-throughput sequencing at the Australian Genome Research Facility (AGRF). Utilising an established pipeline for high-throughput sequencing data analysis, this study revealed the presence of three viruses of the families Circoviridae, Parvoviridae and Adenoviridae. Subsequent sequence comparison and phylogenetic analyses further confirmed that the detected viruses belong to the genera Chaphamaparvovirus (unassigned species), Circovirus (species Circovirus parrot) and Siadenovirus (species Siadenovirus viridis). Despite non-pathogenicity, the existence of multiple viruses within a bird species underscores the risk of these viruses spreading into the pet trade. Detection and a better understanding of avian viruses are crucial for the establishment of appropriate management and biosecurity measures in the domestic and international bird trade, which ultimately supports the conservation of vulnerable bird species.

Effect of polyethylene microplastics on anammox sludge characteristics and microbial communities

Microplastics (MPs), a class of emerging pollutants, pose significant potential risks to microbial metabolism and ecology. By adding different concentration gradients of polyethylene (PE), the effects of PE on the nitrogen removal efficiency and bacterial colony structure of anammox granular sludge (AnGS) were examined. The results of the 40 d experiment with various concentrations (0.05~1.0g/L) of PE MPs on AnGS showed that the elimination of ammonia and nitrite nitrogen was less affected than in the control group. Analysis of extracellular polymer (EPS) content revealed a decreasing trend in EPS content under high stress concentrations (0.2~1.0g/L) compared to the control, and protein/polysaccharide (PN/PS) showed a similar situation, suggesting that the stability of AnGS was compromised by elevated PE concentrations. Specific anammox activity (SAA) indicated a declining trend with increasing polyethylene microplastic (PE MP) concentrations between 0.2 and 1.0g/L, and the nitrogen removal performance of AnGS decreased. From the micro-morphological point of view, the AnGS subjected to 0.5g/L stress formed blocky large particle aggregates. PE MPs were adsorbed on the surface of AnGS and combined with it to form dense particles, and both particle size and mechanical strength of the granular sludge increased proportionally the concentration of PE MPs. High-throughput sequencing data indicated that exposure to 0.5g/L PE MPs stress enhanced the complexity of the microbial community structure in AnGS, while reduced the relative abundance of the anammox bacteria Candidatus Brocadia and Planctomycetes by 2.2% and 4.2%, respectively.

Melittin treatment suppressed malignant NSCLC progression through enhancing CTSB-mediated hyperautophagy

Melittin is preclinically investigated as anticancer agent in multiple tumor types. But its regulation role and regulatory mechanism regarding NSCLC is unknown. In our investigation, Proteomic test was employed to identify proteins that expressed abnormally in cancer cells and that with Melittin treatmented. The results showed CTSB was one of the Top proteins with different expression levels in the lysosomes of Melittin-treatmented cancer cells and showed an up-regulation trend. CTSB expression was increased in NSCLC cancer tissues compared to adjacent normal tissues, as demonstrated in lung cancer tissue chips experiment. However, Melittin treatment increased the CTSB level in lysosomes, which inhibited the malignant progression of NSCLC. We hypothesized that the relative homeostasis of CTSB in cancer cells was destroyed, and CTSB exerts its hydrolytic effect excessively, resulting in excessive autophagy of cancer cells, thus inhibiting the malignant progression of cancer cells. The direct combination of Melittin and CTSB was proposed by molecular docking technique, LiP-SMap was used to analyze the target genes and active components extracted from high-throughput sequencing proteomic data, and successfully verified that melittin was successfully demonstrated to directly target CTSB-binding. In vivo and in vitro studies have shown that Melittin treatment inhibits the malignant progression of A549 and HCC1833 cells and animal tumors, namely non-small cell lung cancer, by promoting CTSB-mediated hyperautophagy. CTSB-specific inhibitor CA-074 Me and autophagy inhibitor 3-MA treatment reversed the inhibit effect of Melittin to the malignant progression of NSCLC. Taken together, Melittin treatment inhibited malignant progression regarding NSCLC through enhancing CTSB-mediated hyperautophagy.

Piezo-traces of ferroelectrics and multiferroics of BiFeO3 -embedded substrate-free Ti3C2Tx MXene films for high throughput data storage application

Piezo-traces of ferroelectrics and multiferroics of BiFeO3 -embedded substrate-free Ti3C2Tx MXene films for high throughput data storage application

Design and characterization of defined alpha-helix mini-proteins with intrinsic cell permeability

Proteins with intrinsic cell permeability that can access intracellular targets represent a promising strategy for novel drug development; however, a general design principle is still lacking. Here, we established a library of 46,678 de novo-designed mini-proteins and performed cell permeability screening via phage display. Analyses revealed a characteristic neighboring distribution of positive charges across helices among enriched mini-proteins of CPP7, CPP11, CPP55, CPP109 and CPP112. Compared with the state-of-the-art cell-penetrating mini-protein ZF5.3, the optimized mini-protein CPP11D36R exhibited a sevenfold increase in cell permeability. Endocytosis uptake and early endosome release are the key penetrating mechanisms. A machine learning model with high-throughput data achieved an F1 score of 0.41, significantly outperforming the previously reported CPP prediction models, including MLACP, CPPpred and CellPPD, by 41 %. Overall, our findings validate the effectiveness of a helical structure with a cationic distribution as a design principle on a large scale and present a robust approach for the development of cell-permeable mini-protein drugs.

Correction: High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Correction: High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Optoelectronic Reconfigurable Logic Gates Based on Two-Dimensional Vertical Field-Effect Transistors.

Optoelectronic reconfigurable logic gates are promising candidates to meet the multifunctional and energy-efficient requirements of integrated circuits. However, complex device architectures need more power and hinder multifunctional device applications. Here, we design vertical field-effect transistors (VFET) based on the two-dimensional (2D) graphene/MoS2/WSe2/graphene van der Waals heterojunction forming ohmic and Schottky contact. The modulation of the Schottky barrier via gate bias enables the device to switch between positive and negative photocurrents, which can effectively achieve optoelectronic reconfigurable logic gates (XNOR, NOR, NAND, AND, OR, and Inhibit) in a single device. Particularly, the transistor number is reduced by 75% for ("XNOR", "NOR", "NAND") and 83% for ("AND", "OR") gates compared to traditional logic circuits. This work provides a promising route for using a single device to realize optoelectronic reconfigurable logic gates, which can advance the development of high-speed, high-throughput, and intricate data processing in future optical computing applications.

Gene Expression Changes in CAD Patients Due to Smoking Using Matched Samples

Abstract Background Smoking is a well-known risk factor for coronary artery disease (CAD). However, the effects of smoking on gene expression in the blood of CAD patients in Hungary have not been extensively studied. Aim To identify differentially expressed genes associated with smoking in CAD patients. Methods Eleven matched samples, based on age and gender, were selected for analysis in this study. All patients were non-obese, non-alcoholic, non-diabetic, and non-hypertensive and had moderate to severe stenosis of one or more coronary arteries, confirmed by coronary angiography. Whole blood samples were collected using PAXgene tubes. Next-generation sequencing was employed using the NextSeq 500 system to generate high-throughput sequencing data for transcriptome profiling. The differentially expressed genes were analyzed using the R programming language. Results The median age of patients was 67 years (range: 54-75). RNA sequencing was performed on two groups: smokers and non-smokers. After quality control and filtering, gene expression data were obtained for all samples. Using DESeq2, we identified 279 differentially expressed genes with a p-value ≤ 0.05 and a log2 fold change ≥1. Of these genes, 160 were upregulated in the smokers, and 119 were downregulated compared to non-smokers. Gene ontology analysis revealed that the upregulated genes were enriched for pathways related to immune responses and activities (FDR&amp;lt; 0.03). Specifically, upregulated genes were involved in keratinocyte differentiation, cornification, and epidermis development. The downregulated genes were enriched for cell-cardiac muscle cell adhesion (FDR= 0.004) and epithelium development (FDR= 0.001) pathways. Conclusions This research illuminates smoking’s biological effects, aiding personalized medicine for predicting and treating smoking-related diseases. Key messages • Smoking alters gene expression in CAD patients’ blood, identifying 279 differentially expressed genes, revealing smoking’s biological effects. • The study underscores gene expression profiles’ role in personalized medicine, predicting smoking-related disease risks and tailoring CAD patient treatments.

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models

This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the collected high-throughput data overcame the applicability limitations, enabling accurate toxicity prediction for a wide range of PC-POPs series compounds using only single models. The data variances in the Rat training and test sets were 1.52 and 1.34, respectively, while for the Mouse, the values were 1.48 and 1.36, respectively. Genetic algorithm (GA) was used to build multiple linear regression (MLR) models and pre-screen descriptors, addressing the “black-box” problem prevalent in ML and enhancing model interpretability. The best ML models for Rat and Mouse achieved approximately 90 % prediction reliability for over 100,000 true untested compounds. Ultimately, a warning list of highly toxic compounds for eight categories of polychlorinated atom-centered fragments (PCACFs) was generated based on the prediction results. The analysis of descriptors revealed that dioxin analogs generally exhibited higher toxicity, because the heteroatoms and ring systems increased structural complexity and formed larger conjugated systems, contributing to greater oral acute toxicity. The present study provides valuable insights for guiding the subsequent in vivo tests, environmental risk assessment and the improvement of global governance system of pollutants.

Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data

BackgroundGene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protein-protein interaction, or metabolic pathways. To analyse gene interaction networks at a large scale, gene co-expression network analysis is often applied on high-throughput gene expression data such as RNA sequencing data. With the advance in sequencing technology, expression of genes can be measured in individual cells. Single-cell RNA sequencing (scRNAseq) provides insights of cellular development, differentiation and characteristics at the transcriptomic level. High sparsity and high-dimensional data structures pose challenges in scRNAseq data analysis.ResultsIn this study, a sparse inverse covariance matrix estimation framework for scRNAseq data is developed to capture direct functional interactions between genes. Comparative analyses highlight high performance and fast computation of Stein-type shrinkage in high-dimensional data using simulated scRNAseq data. Data transformation approaches also show improvement in performance of shrinkage methods in non-Gaussian distributed data. Zero-inflated modelling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in zero-inflated data without interference on non zero-inflated count data.ConclusionThe proposed framework broadens application of graphical model in scRNAseq analysis with flexibility in sparsity of count data resulting from dropout events, high performance, and fast computational time. Implementation of the framework is in a reproducible Snakemake workflow https://github.com/calathea24/ZINBGraphicalModel and R package ZINBStein https://github.com/calathea24/ZINBStein.

Estimating and correcting index hopping misassignments in single-cell RNA-seq data.

Index hopping causes errors in multiplexed high-throughput sequencing data whereby inaccurate barcoding leads to the misassignment of sequence reads to an incorrect source. This can result in gene expression assignments to the wrong cell in single-cell RNA sequencing (scRNA-seq). We performed scRNA-seq on sorted mouse skin cells and identified four distinct cell types based on gene expression profiles. Using genes unique to each cell type, we discovered that approximately 0.65% of total reads per gene were incorrectly assigned to another cell due to index hopping, regardless of gene expression levels. To correct for the misassigned reads, we proportionally adjusted each cell's gene expression data. Applying this correction improved analyses allowing for enhanced cell clustering accuracy and refining the identification of intermediate cell states during cell differentiation. Our study emphasizes the significance of index hopping in scRNA-seq experiments and presents a practical correction approach to remove misassigned reads that can be applied to any scRNA-seq study involving cells with distinct gene expression profiles or where non-expressed genes are introduced as a quality control measure during library preparation.