Diverse immune cells in the tumor microenvironment form a complex ecosystem, but our knowledge of their heterogeneity and dynamics within hepatocellular carcinoma (HCC) still remains limited. To assess the plasticity and phenotypes of immune cells within HBV/HCV-related HCC microenvironment at single-cell level, we performed single-cell RNA sequencing on 41,698 immune cells from seven pairs of HBV/HCV-related HCC tumors and non-tumor liver tissues. We combined bio-informatic analyses, flow cytometry, and multiplex immunohistochemistry to assess the heterogeneity of different immune cell subsets in functional characteristics, transcriptional regulation, phenotypic switching, and interactions. We identified 29 immune cell subsets of myeloid cells, NK cells, and lymphocytes with unique transcriptomic profiles in HCC. A highly complex immunological network was shaped by diverse immune cell subsets that can transit among different states and mutually interact. Notably, we identified a subset of M2 macrophage with high expression of CCL18 and transcription factor CREM that was enriched in advanced HCC patients, and potentially participated in tumor progression. We also detected a new subset of activated CD8+ T cells highly expressing XCL1 that correlated with better patient survival rates. Meanwhile, distinct transcriptomic signatures, cytotoxic phenotypes, and evolution trajectory of effector CD8+ T cells from early-stage to advanced HCC were also identified. Our study provides insight into the immune microenvironment in HBV/HCV-related HCC and highlights novel macrophage and T-cell subsets that could be further exploited in future immunotherapy.

Gene fusions can play important roles in tumor initiation and progression. While fusion detection so far has been from bulk samples, full-length single-cell RNA sequencing (scRNA-seq) offers the possibility of detecting gene fusions at the single-cell level. However, scRNA-seq data have a high noise level and contain various technical artifacts that can lead to spurious fusion discoveries. Here, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. We evaluate the performance of scFusion using simulated and five real scRNA-seq datasets and find that scFusion can efficiently and sensitively detect fusions with a low false discovery rate. In a T cell dataset, scFusion detects the invariant TCR gene recombinations in mucosal-associated invariant T cells that many methods developed for bulk data fail to detect; in a multiple myeloma dataset, scFusion detects the known recurrent fusion IgH-WHSC1, which is associated with overexpression of the WHSC1 oncogene. Our results demonstrate that scFusion can be used to investigate cellular heterogeneity of gene fusions and their transcriptional impact at the single-cell level.

Gene fusions are widespread in tumor cells and can play important roles in tumor initiation and progression. Using full length single cell RNA sequencing (scRNA-seq), gene fusions can now be detected at single cell level by analyzing chimeric reads in scRNA-seq. However, scRNA-seq data has a high noise level and contains various technical artefacts. Direct application of fusion detection tools developed for bulk data can lead to spurious fusion discoveries and leave some true fusions undetected. In this paper, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. scFusion is composed of a statistical model and a deep learning model, both of which are designed to control for potential false discoveries. The statistical model models the background noise as zero inflated negative binomial and uses a statistical testing procedure to control for false positives. The deep learning model is trained to recognize technical chimeric artefacts and filter false fusion candidates generated by these artefacts. We compared scFusion with bulk fusion detection methods using simulation data created based on real scRNA-seq data and found that scFusion had superior performance. Applying scFusion to a T cell data, scFusion successfully detected the invariant TCR gene recombinations in Mucosal-associated invariant T cells that many bulk methods failed to detect. In a multiple myeloma data, scFusion detected the known recurrent fusion IgH-WHSC1, which was associated with overexpression of the WHSC1 oncogene.SignificanceA critical challenge for fusion detection based on the full-length single cell RNA sequencing (scRNA-seq) is to identify the needles, or the true fusions, from a large haystack of false positives. We developed a fusion detection tool scFusion for scRNA-seq. scFusion is computationally more efficient, has far less false discoveries while achieves similar detection power compared to fusion detection tools developed for bulk data. Application of scFusion to a multiple myeloma dataset identied subclones with the fusion IgH-WHSC1 and revealed that over-expression of the oncogene WHSC1 was strongly associated with the fusion. The models developed in this work may also be generalized for other single cell analyses such as structural variation detection and the alternative splicing analysis.