# High resolution tissue and cell type identification via single cell transcriptomic profiling

## TL;DR

This paper introduces scTissueID, a new single-cell RNA sequencing pipeline that improves the accuracy of identifying tissue and cell types in forensic and biomedical research.

## Contribution

The novel contribution is the development of scTissueID, which incorporates cell quality differentiation to enhance tissue and cell type identification.

## Key findings

- scTissueID outperformed 8 existing pipelines and 6 machine learning algorithms in cell and tissue type identification.
- The study demonstrated the importance of cell quality differentiation, a previously undervalued factor in single-cell analysis.
- The tool is applicable to forensic investigations and biomedical research.

## Abstract

Tissue identification can be instrumental in reconstructing a crime scene but remains a challenging task in forensic investigations. Conventionally, identifying the presence of certain tissue from tissue mixture by predefined cell type markers in bulk fashion is challenging due to limitations in sensitivity and accuracy. In contrast, single-cell RNA sequencing (scRNA-Seq) is a promising technology that has the potential to enhance or even revolutionize tissue and cell type identification. In this study, we developed a high sensitive general purpose single cell annotation pipeline, scTissueID, to accurately evaluate the single cell profile quality and precisely determine the cell and tissue types based on scRNA profiles. By incorporating a crucial and unique reference cell quality differentiation phase of targeting only high confident cells as reference, scTissueID achieved better and consistent performance in determining cell and tissue types compared to 8 state-of-art single cell annotation pipelines and 6 widely adopted machine learning algorithms, as demonstrated through a large-scale and comprehensive comparison study using both forensic-relevant and Human Cell Atlas (HCA) data. We highlighted the significance of cell quality differentiation, a previously undervalued factor. Thus, this study offers a tool capable of accurately and efficiently identifying cell and tissue types, with broad applicability to forensic investigations and other biomedical research endeavors.