Field Of Biology Research Articles

The influence of spheroid maturity on fusion dynamics and micro-tissue assembly in 3D tumor models.

Three-dimensional (3D) cell culture has been used in many fields of biology because of its unique advantages. As a representative of the 3D systems, 3D spheroids are used as building blocks for tissue construction. Larger tumor aggregates can be assembled by manipulating or stacking the tumor spheroids. The motivation of this study is to investigate the behavior of the cells distributed at different locations of the spheroids in the fusion process and the mechanism behind it. To this aim, spheroids with varying grades of maturity or age were generated for fusion to assemble micro-tumor tissues. The dynamics of the fusion process, the motility of the cells distributed in different heterogeneous architecture sites, and their reactive oxygen species profiles were studied. We found that the larger the spheroid necrotic core, the slower the fusion rate of the spheroid. The cells that move are mainly distributed on the spheroid's surface during fusion. In addition to dense microfilament distribution and low microtubule content, the reactive oxygen content is high in the fusion site, while the non-fusion site is the opposite. Last, multi-spheroids with different maturities were fused to complex micro-tissues to mimic solid tumors and evaluate Doxorubicin's anti-tumor efficacy.

The Relationship between Concept Perception and Student Achievement in the Subject Area of Chemistry in Science

Science education has undergone many reforms since the past to improve the learning and teaching process. Teachers' knowledge and motivation, and teachers' teaching techniques, determine how students understand science concepts. In the journey of taking students to innovations in education, the teacher who provides science education in the classroom has a great responsibility (Research and Development Branch, 2017). It is also necessary to identify the problems faced by students in building science education. The analysis of the examination department's results and evaluation reports shows that compared to other subjects, the minimum mark is obtained for the science subject. The Sri Lanka Examination Department's 2017 G.E.C. (G.E.) evaluation report also shows that among the three fields of science, biology, chemistry, and physics, the facility for the field of chemistry is low. For this reason, it was the main interest of this research to find out the correlation between student performance and conceptual understanding of students who learn the content of the chemistry subject of the science subject. For that purpose, the size of the sample was determined using the table of determination of the sample size presented by Krejcie & Morgan (1970) and the correlational study research strategy using 400 students of 08 secondary schools in the Kegalle district of Sri Lanka selected through a cluster random sampling method as the sample. The research was conducted using a quantitative design. For this, Katerina Salta & Chryssa Tzougraki (2004) used an aptitude test set according to the Liket scale and a structured type of achievement test set according to Bloom's classification to find out the level of concept perception of 11th grade students in a school in Greece. A control test was administered to 10% of the research instrument sample. By analyzing the data collected, the research instruments were standardized. Then by giving the research equipment to the main sample, the collected data was analyzed, and conclusions were drawn from the data interpretation. Here, a positive correlation of Pearson correlation coefficient (r= + 0.720) was found between students' conceptual understanding and student achievement. It was recognized that appropriate student-centered methods should be used in the learning and teaching process as student achievement increases when students' conceptual understanding increases.

Spatial Atlas for Mapping Vascular Microcalcification Using 18F-NaF PET/CT: Application in Hyperphosphatemic Familial Tumoral Calcinosis.

Vascular calcification causes significant morbidity and occurs frequently in diseases of calcium/phosphate imbalance. Radiolabeled sodium fluoride positron emission tomography/computed tomography has emerged as a sensitive and specific method for detecting and quantifying active microcalcifications. We developed a novel technique to quantify and map total vasculature microcalcification to a common space, allowing simultaneous assessment of global disease burden and precise tracking of site-specific microcalcifications across time and individuals. To develop this technique, 4 patients with hyperphosphatemic familial tumoral calcinosis, a monogenic disorder of FGF23 (fibroblast growth factor-23) deficiency with a high prevalence of vascular calcification, underwent radiolabeled sodium fluoride positron emission tomography/computed tomography imaging. One patient received serial imaging 1 year after treatment with an IL-1 (interleukin-1) antagonist. A radiolabeled sodium fluoride-based microcalcification score, as well as calcification volume, was computed at all perpendicular slices, which were then mapped onto a standardized vascular atlas. Segment-wise mCSmean and mCSmax were computed to compare microcalcification score levels at predefined vascular segments within subjects. Patients with hyperphosphatemic familial tumoral calcinosis had notable peaks in microcalcification score near the aortic bifurcation and distal femoral arteries, compared with a control subject who had uniform distribution of vascular radiolabeled sodium fluoride uptake. This technique also identified microcalcification in a 17-year-old patient, who had no computed tomography-defined calcification. This technique could not only detect a decrease in microcalcification score throughout the patient treated with an IL-1 antagonist but it also identified anatomic areas that had increased responsiveness while there was no change in computed tomography-defined macrocalcification after treatment. This technique affords the ability to visualize spatial patterns of the active microcalcification process in the peripheral vasculature. Further, this technique affords the ability to track microcalcifications at precise locations not only across time but also across subjects. This technique is readily adaptable to other diseases of vascular calcification and may represent a significant advance in the field of vascular biology.

[Experimental teaching design of CRISPR/Cas9 technology in rice breeding application].

The CRISPR/Cas9 gene editing technology has proven to be valuable in crop breeding applications. Understanding and mastering this technology will provide a strong foundation for students majoring in biology, agronomy, and related fields to engage in scientific research and work. To incorporate CRISPR/Cas9 technology into experimental teaching courses at colleges, an innovative teaching experiment entitled "Enhancing the resistance of rice plants to bacterial blight disease using CRISPR/Cas9 technology" was designed. The experiment allows students to deepen their understanding of the basic principles of CRISPR/Cas technology, acquire proficiency in its protocol, and learn to apply the technology for targeted molecular breeding of rice. It not only expands students' knowledge and skills, but also promotes the reform and innovation of experimental teaching methods.

Multiplex genetic manipulations in Clostridium butyricum and Clostridium sporogenes to secrete recombinant antigen proteins for oral-spore vaccination

BackgroundClostridium spp. has demonstrated therapeutic potential in cancer treatment through intravenous or intratumoral administration. This approach has expanded to include non-pathogenic clostridia for the treatment of various diseases, underscoring the innovative concept of oral-spore vaccination using clostridia. Recent advancements in the field of synthetic biology have significantly enhanced the development of Clostridium-based bio-therapeutics. These advancements are particularly notable in the areas of efficient protein overexpression and secretion, which are crucial for the feasibility of oral vaccination strategies. Here, we present two examples of genetically engineered Clostridium candidates: one as an oral cancer vaccine and the other as an antiviral oral vaccine against SARS-CoV-2.ResultsUsing five validated promoters and a signal peptide derived from Clostridium sporogenes, a series of full-length NY-ESO-1/CTAG1, a promising cancer vaccine candidate, expression vectors were constructed and transformed into C. sporogenes and Clostridium butyricum. Western blotting analysis confirmed efficient expression and secretion of NY-ESO-1 in clostridia, with specific promoters leading to enhanced detection signals. Additionally, the fusion of a reported bacterial adjuvant to NY-ESO-1 for improved immune recognition led to the cloning difficulties in E. coli. The use of an AUU start codon successfully mitigated potential toxicity issues in E. coli, enabling the secretion of recombinant proteins in C. sporogenes and C. butyricum. We further demonstrate the successful replacement of PyrE loci with high-expression cassettes carrying NY-ESO-1 and adjuvant-fused NY-ESO-1, achieving plasmid-free clostridia capable of secreting the antigens. Lastly, the study successfully extends its multiplex genetic manipulations to engineer clostridia for the secretion of SARS-CoV-2-related Spike_S1 antigens.ConclusionsThis study successfully demonstrated that C. butyricum and C. sporogenes can produce the two recombinant antigen proteins (NY-ESO-1 and SARS-CoV-2-related Spike_S1 antigens) through genetic manipulations, utilizing the AUU start codon. This approach overcomes challenges in cloning difficult proteins in E. coli. These findings underscore the feasibility of harnessing commensal clostridia for antigen protein secretion, emphasizing the applicability of non-canonical translation initiation across diverse species with broad implications for medical or industrial biotechnology.

Advanced computational approaches to understand protein aggregation.

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Modulating Nitric Oxide: Implications for Cytotoxicity and Cytoprotection

Despite the significant progress in the fields of biology, physiology, molecular medicine, and pharmacology; the designation of the properties of nitrogen monoxide in the regulation of life-supporting functions of the organism; and numerous works devoted to this molecule, there are still many open questions in this field. It is widely accepted that nitric oxide (•NO) is a unique molecule that, despite its extremely simple structure, has a wide range of functions in the body, including the cardiovascular system, the central nervous system (CNS), reproduction, the endocrine system, respiration, digestion, etc. Here, we systematize the properties of •NO, contributing in conditions of physiological norms, as well as in various pathological processes, to the mechanisms of cytoprotection and cytodestruction. Current experimental and clinical studies are contradictory in describing the role of •NO in the pathogenesis of many diseases of the cardiovascular system and CNS. We describe the mechanisms of cytoprotective action of •NO associated with the regulation of the expression of antiapoptotic and chaperone proteins and the regulation of mitochondrial function. The most prominent mechanisms of cytodestruction—the initiation of nitrosative and oxidative stresses, the production of reactive oxygen and nitrogen species, and participation in apoptosis and mitosis. The role of •NO in the formation of endothelial and mitochondrial dysfunction is also considered. Moreover, we focus on the various ways of pharmacological modulation in the nitroxidergic system that allow for a decrease in the cytodestructive mechanisms of •NO and increase cytoprotective ones.

Bayesian compositional models for ordinal response.

Ordinal response is commonly found in medicine, biology, and other fields. In many situations, the predictors for this ordinal response are compositional, which means that the sum of predictors for each sample is fixed. Examples of compositional data include the relative abundance of species in microbiome data and the relative frequency of nutrition concentrations. Moreover, the predictors that are strongly correlated tend to have similar influence on the response outcome. Conventional cumulative logistic regression models for ordinal responses ignore the fixed-sum constraint on predictors and their associated interrelationships, and thus are not appropriate for analyzing compositional predictors.To solve this problem, we proposed Bayesian Compositional Models for Ordinal Response to analyze the relationship between compositional data and an ordinal response with a structured regularized horseshoe prior for the compositional coefficients and a soft sum-to-zero restriction on coefficients through the prior distribution. The method was implemented with R package rstan using efficient Hamiltonian Monte Carlo algorithm. We performed simulations to compare the proposed approach and existing methods for ordinal responses. Results revealed that our proposed method outperformed the existing methods in terms of parameter estimation and prediction. We also applied the proposed method to a microbiome study HMP2Data, to find microorganisms linked to ordinal inflammatory bowel disease levels. To make this work reproducible, the code and data used in this paper are available at https://github.com/Li-Zhang28/BCO.

An Anthocyanin-Based Eco-Friendly Triboelectric Nanogenerator for pH Monitoring and Energy Harvesting

In recent years, renewable and sustainable triboelectric nanogenerators have attracted attention due to their high energy conversion rate, and enhancing their functionality further contributes to their applicability across various fields. A pH-sensitive triboelectric nanogenerator (pH-TENG) has been prepared by electrostatic spinning technology, with anthocyanin as the pH indicator and environmentally friendly polyvinyl alcohol (PVA) as the substrate. Among many friction-negative materials, the pH-TENG exhibits the best combination with fluorinated ethylene propylene (FEP) and yields an open-circuit voltage of 62 V, a short-circuit current of 370 nA, and a transferred charge of 21.8 nC. At a frequency of 3 Hz, it can charge a 4.7 μF capacitor to 2 V within 45 s, effectively powering a thermometer. Furthermore, the presence of anthocyanin does not affect the pH-TENG’s power generation performance and enables the monitoring of a wide range of environmental pH changes, with an ΔE change of 28.8 ± 7.6. Therefore, pH-TENG prepared with environmentally friendly materials can bring new available materials to the biological and medical fields.

Viewing early life without labels: optical approaches for imaging the early embryo.

Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high-resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: 1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, 2) highlight the recent application of these technologies in the field of reproductive biology, and 3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.

A comprehensive dataset of protein-protein interactions and ligand binding pockets for advancing drug discovery

This dataset represents a collection of pocket-centric structural data related to protein-protein interactions (PPIs) and PPI-related ligand binding sites. The dataset includes high-quality structural information on more than 23,000 pockets, 3,700 proteins on more than 500 organisms, and nearly 3500 ligands that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes with more than 1,700 unique protein families including some with associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for hypothesizing protein partners repurposing.

Induced-volatolomics, a new research field in chemical biology

Induced-volatolomics, a new research field in chemical biology

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

Encoding biological metaverse: Advancements and challenges in neural fields from macroscopic to microscopic

Neural fields can efficiently encode three-dimensional (3D) scenes, providing a bridge between two-dimensional (2D) images and virtual reality. This method becomes a trendsetter in bringing the metaverse into vivo life. It has initially captured the attention of macroscopic biology, as demonstrated by computed tomography and magnetic resonance imaging, which provide a 3D field of view for diagnostic biological images. Meanwhile, it has also opened up new research opportunities in microscopic imaging, such as achieving clearer de novo protein structure reconstructions. Introducing this method to the field of biology is particularly significant, as it is refining the approach to studying biological images. However, many biologists have yet to fully appreciate the distinctive meaning of neural fields in transforming 2D images into 3D perspectives. This article discusses the application of neural fields in both microscopic and macroscopic biological images and their practical uses in biomedicine, highlighting the broad prospects of neural fields in the future biological metaverse. We stand at the threshold of an exciting new era, where the advancements in neural field technology herald the dawn of exploring the mysteries of life in innovative ways.

The distributional properties of long nominal compounds in scientific articles: an investigation based on the uniform information density hypothesis

Abstract Nominal compounds are a structure commonly used in scientific texts. Despite their commonality, very little is known about how they are distributed in scientific articles. Based on the Uniform Information Density hypothesis, which states that speakers communicate information at a constant rate, avoiding peaks and troughs of information transmission, we predict that nominal compounds should cluster toward the end of scientific texts, be preceded by supporting text that facilitates their understanding, and be repeated often after their first use. In this paper, we examine these predictions through a quantitative and a qualitative analysis of a corpus of scientific papers from the fields of Biology, Economics and Linguistics. While our investigation did not reveal definitive findings for the first and third predictions above, it did produce supporting evidence in favor of our second prediction, thus advancing our understanding of NC use and the choices speakers make when transmitting information.

The Net of Life, a short story: Intricate patterns of gene flows across hundreds of extant genomes, all the way to LUCA

Over the past quarter-century, the field of evolutionary biology has been transformed by the emergence of complete genome sequences and the conceptual framework known as the 'Net of Life.' This paradigm shift challenges traditional notions of evolution as a tree-like process, emphasizing the complex, interconnected network of gene flow that may blur the boundaries between distinct lineages. In this context, gene loss, rather than horizontal gene transfer, is the primary driver of gene content, with vertical inheritance playing a principal role. The 'Net of Life' not only impacts our understanding of genome evolution but also has profound implications for classification systems, the rapid appearance of new traits, and the spread of diseases. Here, we explore the core tenets of the 'Net of Life' and its implications for genome-scale phylogenetic divergence, providing a comprehensive framework for further investigations in evolutionary biology.

Tunable zero-field magnetoresistance responses in Si transistors: Origins and applications

The near-zero-field magnetoresistance (NZFMR) response has proven to be a useful tool for studying atomic-scale, paramagnetic defects that are relevant to the reliability of semiconductor devices. The measurement is simple to make and, in some cases, simple to interpret. In other cases, more sophisticated modeling based on the stochastic Liouville equation (SLE) is needed to access valuable information from NZFMR results. It has been shown that hyperfine and dipolar coupling interactions at atomic-scale defects affect the NZFMR line shape, but experimental parameters related to the detection method of NZFMR can also affect the nature of the response. Here, we demonstrate four distinct NZFMR detection methods in Si MOSFETs, which all access identical Si/SiO2 interface defects. In all four cases, we show that the line shape of the response is tunable based on experimental parameters alone. Using SLE-based modeling, we verify that time constants connected to physical carrier capture rates at the defect sites lead to these NZFMR line shape changes. The results demonstrate a method to extract some atomic-scale information for the purpose of defect identification. They also have broader applications to the continued development of ultra-sensitive magnetometers based on NZFMR in semiconductors. Additionally, the NZFMR effect in common Si-based devices may provide an inexpensive and accessible platform that mimics similar radical pair mechanisms that have become increasingly important in various biology fields.

All‐Printed Electrically Driven Lighting via Electrochemiluminescence

AbstractElectrochemiluminescence (ECL) has attracted significant attention as a promising light‐emitting technology owing to its simple configuration and solution processability. ECL has wide applications in biological and chemical sensors, lighting, and displays. ECL lighting devices are fabricated as sandwiched structures with a transparent electrode on top, which limits their widespread adoption. Intricate structures with high precision and customizable designs can be directly fabricated using 3D printing. However, 3D printing of ECL devices is challenging because of poor printability and structural integrity of ECL luminophore inks. Here, all‐printed electrically driven lighting is demonstrated using the direct ink writing method. The ECL reactive layer and electrodes are directly printed using a side‐by‐side electrode configuration. A 3D printable ECL ink is developed by incorporating silica nanoparticles as rheological modifiers to realize well‐defined patterns with high structural integrity. Graphene is introduced on Ag multilayer electrodes to embrace both the electrical conductivity and electrochemical stability for efficient ECL operation. This all‐printing approach allows the fabrication of ECL devices with complex designs, such as combs and spirals. Moreover, it facilitates seamless printing on various substrate materials, making this technology an asset in the fields of biology, chemistry, and electronics.

Investigation of metal ion binding biomolecules one molecule at a time.

Metal ions can perform multiple roles ranging from regulatory to structural and are crucial for cell function. While some metal ions like Na+ are ubiquitously present at high concentrations, other ions, especially Ca2+ and transition metals, such as Zn2+ or Cu+/2+ are regulated. The concentrations above or below the physiological range cause severe changes in the behavior of biomolecules that bind them and subsequently affect the cell wellbeing. This has led to the development of specialized protocols to study metal ion binding biomolecules in bulk conditions that mimic the cell environment. Recently, there is growing evidence of influence of post-transcriptional and post-translational modifications on the affinity of the metal ion binding sites. However, such targets are difficult to obtain in amounts required for classical biophysical experiments. Single molecule techniques have revolutionized the field of biophysics, molecular and structural biology. Their biggest advantage is the ability to observe each molecule's interaction independently, without the need for synchronization. An additional benefit is its extremely low sample consumption. This feature allows characterization of designer biomolecules or targets obtained coming from natural sources. All types of biomolecules, including proteins, DNA and RNA were characterized using single molecule methods. However, one group is underrepresented in those studies. These are the metal ion binding biomolecules. Single molecule experiments often require separate optimization, due to extremely different concentrations used during the experiments. In this review we focus on single molecule methods, such as single molecule FRET, nanopores and optical tweezers that are used to study metal ion binding biomolecules. We summarize various examples of recently characterized targets and reported experimental conditions. Finally, we discuss the potential promises and pitfalls of single molecule characterization on metal ion binding biomolecules.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

BackgroundSingle-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.ResultsWe show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.ConclusionOur comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.