Tailoring the resolution of single-cell RNA sequencing for primary cytotoxic T cells

Heat Inactivation of Nipah Virus for Downstream Single-Cell RNA Sequencing Does Not Interfere with Sample Quality

Single-cell RNA sequencing (scRNA-seq) technologies are instrumental to improving our understanding of virus-host interactions in cell culture infection studies and complex biological systems because they allow separating the transcriptional signatures of infected versus non-infected bystander cells. A drawback of using biosafety level (BSL) 4 pathogens is that protocols are typically developed without consideration of virus inactivation during the procedure. To ensure complete inactivation of virus-containing samples for downstream analyses, an adaptation of the workflow is needed. Focusing on a commercially available microfluidic partitioning scRNA-seq platform to prepare samples for scRNA-seq, we tested various chemical and physical components of the platform for their ability to inactivate Nipah virus (NiV), a BSL-4 pathogen that belongs to the group of nonsegmented negative-sense RNA viruses. The only step of the standard protocol that led to NiV inactivation was a 5 min incubation at 85 °C. To comply with the more stringent biosafety requirements for BSL-4-derived samples, we included an additional heat step after cDNA synthesis. This step alone was sufficient to inactivate NiV-containing samples, adding to the necessary inactivation redundancy. Importantly, the additional heat step did not affect sample quality or downstream scRNA-seq results.

Establishment of an Inactivation Method for Ebola Virus and SARS-CoV-2 Suitable for Downstream Sequencing of Low Cell Numbers

Technologies that facilitate the bulk sequencing of small numbers of cells as well as single-cell RNA sequencing (scRNA-seq) have aided greatly in the study of viruses as these analyses can be used to differentiate responses from infected versus bystander cells in complex systems, including in organoid or animal studies. While protocols for these analyses are typically developed with biosafety level 2 (BSL-2) considerations in mind, such analyses are equally useful for the study of viruses that require higher biosafety containment levels. Many of these workstreams, however, are not directly compatible with the more stringent biosafety regulations of BSL-3 and BSL-4 laboratories ensuring virus inactivation and must therefore be modified. Here we show that TCL buffer (Qiagen), which was developed for bulk sequencing of small numbers of cells and also facilitates scRNA-seq, inactivates both Ebola virus (EBOV) and SARS-CoV-2, BSL-4 and BSL-3 viruses, respectively. We show that additional heat treatment, necessary for the more stringent biosafety concerns for BSL-4-derived samples, was additionally sufficient to inactivate EBOV-containing samples. Critically, this heat treatment had minimal effects on extracted RNA quality and downstream sequencing results.

Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma

Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear. Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated. Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response. Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.

Improving Transcriptome Fidelity Following Synovial Tissue Disaggregation

Objective

To improve the fidelity of the cellular transcriptome of disaggregated synovial tissue for applications such as single-cell RNA sequencing (scRNAseq) by modifying the disaggregation technique.

Methods

Osteoarthritis (OA) and rheumatoid arthritis (RA) synovia were collected at arthroplasty. RNA was extracted from intact or disaggregated replicate pools of tissue fragments. Disaggregation was performed with either a proprietary protease, Liberase TL (Lib) as a reference method, Liberase TL with an RNA polymerase inhibitor flavopyridol (Flavo), or a cold digestion with subtilisin A (SubA). qPCR on selected markers and RNAseq were used to compare disaggregation methods using the original intact tissue as reference.

Results

Disaggregated cell yield and viability were similar for all three methods with some viability improved (SubA). Candidate gene analysis showed that Lib alone dramatically increased expression of several genes involved in inflammation and immunity compared with intact tissue and was unable to differentiate RA from OA. Both alternative methods reduced the disaggregation induced changes. Unbiased analysis using bulk RNAseq and the 3 protocols confirmed the candidate gene studies and showed that disaggregation-induced changes were largely prevented. The resultant data improved the ability to distinguish RA from OA synovial transcriptomes.

Conclusions

Disaggregation of connective tissues such as synovia has complex and selective effects on the transcriptome. We found that disaggregation with an RNA polymerase inhibitor or using a cold enzyme tended to limit induction of some relevant transcripts during tissue processing. The resultant data in the disaggregated transcriptome better represented the in situ transcriptome. The specific method chosen can be tailored to the genes of interest and the hypotheses being tested in order to optimize the fidelity of technique for applications based on cell suspensions such as sorted populations or scRNAseq.

Molecular spikes: a gold standard for single-cell RNA counting

Single-cell sequencing methods rely on molecule-counting strategies to account for amplification biases, yet no experimental strategy to evaluate counting performance exists. Here, we introduce molecular spikes—RNA spike-ins containing built-in unique molecular identifiers (UMIs) that we use to identify critical experimental and computational conditions for accurate RNA counting in single-cell RNA-sequencing (scRNA-seq). Using molecular spikes, we uncovered impaired RNA counting in methods that were not informative for cellular RNA abundances due to inflated UMI counts. We further leverage molecular spikes to improve estimates of total endogenous RNA amounts in cells, and introduce a strategy to correct experiments with impaired RNA counting. The molecular spikes and the accompanying R package UMIcountR (https://github.com/cziegenhain/UMIcountR) will improve the validation of new methods, better estimate and adjust for cellular mRNA amounts and enable more indepth characterization of RNA counting in scRNA-seq.

This work presents an RNA spike-in that can be used to improve RNA counting in single-cell RNA-sequencing (scRNA-seq) analysis, as well as to report the performance of scRNA-seq methods.

Staggered starts in the race to T cell activation

How T lymphocytes tune their responses to different strengths of stimulation is a fundamental question in immunology. Recent work using new optogenetic, single-cell genomic, and live-imaging approaches has revealed that stimulation strength controls the rate of individual cell responses within a population. Moreover, these responses have been found to use shared molecular programs, regardless of stimulation strength. However, additional data indicate that stimulation duration or cytokine feedback can impact later gene expression phenotypes of activated cells. In-depth molecular studies have suggested mechanisms by which stimulation strength might modulate the probability of T cell activation. This emerging model allows activating T cells to achieve a wide range of population responses through probabilistic control within individual cells.

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.