Biology subjects

Ben-Yehezkel, T.

Publications and source records attributed to Ben-Yehezkel, T..

3 recordsLinked to original sources

bioRxiv2025

Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal cancers for humans. HCC is highly heterogeneous. In this study, we performed ultra-depth ([~]1 million reads per spot) spatial sequencing on a case of HCC. Sixteen distinct spatial expression clusters were identified. Each of these clusters was spatially contiguous and had distinct gene expression patterns. In contrast, benign liver tissues showed minimal heterogeneity in terms of gene expression. Numerous immune cell-enriched spots were identified in both HCC and benign liver regions. Cells adjacent to these immune cell-enriched spots showed significant alterations in their gene expression patterns. Interestingly, the responses of HCC cells to the nearby immune cells were significantly more intense and broader, while the responses of benign liver cells to immune cells were somewhat narrow and muted, suggesting an innate difference in immune cell activities towards HCC cells in comparison with benign liver cells. When standard-depth sequencing was performed, significant numbers of genes and pathways that were associated with these changes disappeared. Qualitative differences in some pathways were also found. These results suggest that deep spatial sequencing may help to uncover previously unidentified mechanisms of liver cancer development.

cancer biology↗

bioRxiv2024

Utility Analyses of AVITI Sequencing Chemistry

BackgroundDNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. ResultsHere, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences AVITI and Illuminas NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illuminas NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. ConclusionThese results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

genomics↗

bioRxiv2019

A Single-Molecule Long-Read Survey of Human Transcriptomes using LoopSeq Synthetic Long Read Sequencing

State-of-the-art short-read transcriptome sequencing methods employ unique molecular identifier (UMI) to accurately classify and count mRNA transcripts. A fundamental limitation of UMI-based short-read transcriptome sequencing is that each read typically covers a small fraction of the transcript sequence. Efforts to accurately characterize splicing isoforms, arguably the largest source of variation in Human gene expression, using short read sequencing have therefore largely relied on computational predictions of transcript isoforms based on indirect observations. Here we describe a transcript counting, synthetic long read method for sequencing whole transcriptomes using short read sequencing platforms and no additional hardware. The method enables full-length mRNA sequence reconstruction at single-nucleotide resolutions with high-throughput, low error rates and UMI based transcript counting using any Illumina sequencer. We describe results from whole transcriptome sequencing from total RNA extracted from 3 human tissue samples: brain, liver, and blood. Reconstructed transcript sequences are characterized and annotated using SQANTI, an analysis pipeline for assessing the sequence quality of long-read transcriptomes. Our results demonstrate that LoopSeq synthetic long-read sequencing can reconstruct contigs up to 3,900nt full-length transcripts using tissue extracted RNA, as well as identify novel splice variants of known junction donors and acceptors.

genomics↗