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

BACKGROUND: Single-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. RESULTS: We 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. CONCLUSION: Our 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.

High throughput identification of human monoclonal antibodies and heavy-chain-only antibodies to treat snakebite.

Snakebite envenoming is a priority Neglected Tropical Disease that causes an estimated 81,000-135,000 fatalities each year. The development of a new generation of safer, affordable, and accessible antivenom therapies is urgently needed. With this goal in mind, rigorous characterisation of the specific toxins in snake venom is key to generating novel therapies for snakebite. Monoclonal antibodies directed against venom toxins are emerging as potentially strong candidates in the development of new snakebite diagnostics and treatment. Venoms comprise many different toxins of which several are responsible for their pathological effects. Due to the large variability of venoms within and between species, formulations of combinations of human antibodies are proposed as the next generation antivenoms. Here a high-throughput screening method employing antibody-based ligand fishing of venom toxins in 384 filter-well plate format has been developed to determine the antibody target/s The approach uses Protein G beads for antibody capture followed by exposure to a full venom or purified toxins to bind their respective ligand toxin(s). This is followed by a washing/centrifugation step to remove non-binding toxins and an in-well tryptic digest. Finally, peptides from each well are analysed by nanoLC-MS/MS and subsequent Mascot database searching to identify the bound toxin/s for each antibody under investigation. The approach was successfully validated to rapidly screen antibodies sourced from hybridomas, derived from venom-immunised mice expressing either regular human antibodies or heavy-chain-only human antibodies (HCAbs).

Neutralizing antibodies reveal cryptic vulnerabilities and interdomain crosstalk in the porcine deltacoronavirus spike protein.

Porcine deltacoronavirus (PDCoV) is an emerging enteric pathogen that has recently been detected in humans. Despite this zoonotic concern, the antigenic structure of PDCoV remains unknown. The virus relies on its spike (S) protein for cell entry, making it a prime target for neutralizing antibodies. Here, we generate and characterize a set of neutralizing antibodies targeting the S protein, shedding light on PDCoV S interdomain crosstalk and its vulnerable sites. Among the four identified antibodies, one targets the S1A domain, causing local and long-range conformational changes, resulting in partial exposure of the S1B domain. The other antibodies bind the S1B domain, disrupting binding to aminopeptidase N (APN), the entry receptor for PDCoV. Notably, the epitopes of these S1B-targeting antibodies are concealed in the prefusion S trimer conformation, highlighting the necessity for conformational changes for effective antibody binding. The binding footprint of one S1B binder entirely overlaps with APN-interacting residues and thus targets a highly conserved epitope. These findings provide structural insights into the humoral immune response against the PDCoV S protein, potentially guiding vaccine and therapeutic development for this zoonotic pathogen.

Filamentous fungus-produced human monoclonal antibody provides protection against SARS-CoV-2 in hamster and non-human primate models.

Monoclonal antibodies are an increasingly important tool for prophylaxis and treatment of acute virus infections like SARS-CoV-2 infection. However, their use is often restricted due to the time required for development, variable yields and high production costs, as well as the need for adaptation to newly emerging virus variants. Here we use the genetically modified filamentous fungus expression system Thermothelomyces heterothallica (C1), which has a naturally high biosynthesis capacity for secretory enzymes and other proteins, to produce a human monoclonal IgG1 antibody (HuMab 87G7) that neutralises the SARS-CoV-2 variants of concern (VOCs) Alpha, Beta, Gamma, Delta, and Omicron. Both the mammalian cell and C1 produced HuMab 87G7 broadly neutralise SARS-CoV-2 VOCs in vitro and also provide protection against VOC Omicron in hamsters. The C1 produced HuMab 87G7 is also able to protect against the Delta VOC in non-human primates. In summary, these findings show that the C1 expression system is a promising technology platform for the development of HuMabs in preventive and therapeutic medicine.