Genomic expression analysis by single-cell mRNA differential display of quiescent CD8 T cells from tumour-infiltrating lymphocytes obtained from in vivo liver tumours

Personalized Immunotherapy of Patients: Defining by Single-cell RNA-seq with Artificial Intelligence

Immunotherapy, including immune cell therapy and targeted therapy, is gradually developed through the ongoing discovery of molecular compounds or immune cells. Choosing the best one or the best combination of target compounds and immune-cell therapy is a challenge for clinical scientists and clinicians. We have found variable efficacy individually after tumor-infiltrating lymphocyte (TIL) therapy, and now TILs have been discovered in a group of heterogeneous immune cells. To select the best immunotherapy for each patient, we started to study TIL genomics, including single-cell mRNA differential display from TIL published in 2007 and single-cell RNA-seq from TIL published in 2013, set up TIL quantitative network in 2015, researched machine-learning model for immune therapy in 2022. These manual reports single-cell RNA-seq data combined with machine learning to evaluate the optimal compounds and immune cells for individual patients. The machine-learning model, one of artificial intelligence, can estimate targeting genomic variance from single-cell RNA-seq so that they can cover thirteen kinds of immune cell therapies and ongoing FDA-approved targeted therapies such as PD1 inhibitors, PDL1 inhibitors, and CTLA4 inhibitors, as well as other different treatments such as HDACI or DNMT1 inhibitors, FDA-approved drugs. Moreover, also cover Phase-1, Phase-2, Phase-3, and Phase-4 of clinical trials, such as TIL, CAR T-cells, TCR T-cells. Single-cell RNA-seq with an Artificial intelligence estimation system is much better than our published models from microarrays or just cell therapy. The medical goal is to address three issues in clinical immunotherapy: the increase of efficacy; the decrease of adverse effects and the decrease of the cost in clinical applications.

Machine-learning Modeling for Personalized Immunotherapy- An Evaluation Module

Immune-cell therapy and targeting therapy are in rapid development to treat tumor diseases. However, current immune-cell therapy and targeting immunotherapy often face three challenges (three Ss): safety challenges such as cytokine releasing syndrome (C.R.S.); specificity targeting problems such as low efficacy caused by off-targeting tumor cells; unsatisfying payment are confounded to clinical patients and physicians. We have been studying immunotherapy for more than thirty years, and recently, personalized immunotherapy to treat tumor disease has been proposed. After we discovered quiescent genes from immune cells within the tumor microenvironment, we set up single-cell genomics analysis, studying heterogeneous immune responses from multiple tumor antigens (neo-antigen); here, we further introduce a new generation of immunotherapy module by using a machine-learning model to assess optimal immunotherapy. The machine-learning model combined with single-cell genomic analysis can predict optimal immune-cell (such as T-cells) and other optimal targeting drugs such as PD1 and CTLA4 inhibitors for the patient to use.

Why do tumor-infiltrating lymphocytes have variable efficacy in the treatment of solid tumors?

Lymphocytes in tumor tissue are called tumor-infiltrating lymphocytes (TILs), and they play a key role in the control and treatment of tumor diseases. Since the discovery in 1987 that cultured TILs can kill tumor cells more than 100 times more effectively than T-cells cultured from peripheral blood in melanoma, it has been confirmed that cultured TILs can successfully cure clinical patients with melanoma. Since 1989, after we investigated TIL isolation performance from solid tumors, we modified some procedures to increase efficacy, and thus successfully established new TIL isolation and culture methods in 1994. Moreover, our laboratory and clinicians using our cultured TILs have published more than 30 papers. To improve the efficacy of TILs, we have been carrying out studies of TIL efficacy to treat solid tumor diseases for approximately 30 years. The three main questions of TIL study have been "How do TILs remain silent in solid tumor tissue?", "How do TILs attack homologous and heterologous antigens from tumor cells of solid tumors?", and "How do TILs infiltrate solid tumor tissue from a distance into tumor sites to kill tumor cells?". Research on these three issues has increasingly answered these questions. In this review I summarize the main issues surrounding TILs in treating solid tumors. This review aims to study the killing function of TILs from solid tumor tissues, thereby ultimately introducing the optimal strategy for patients suffering from solid tumors through personalized immunotherapy in the near future.

Feasibility of whole RNA sequencing from single-cell mRNA amplification

Single-cell sampling with RNA-seq analysis plays an important role in reference laboratory; cytogenomic diagnosis for specimens on glass-slides or rare cells in circulating blood for tumor and genetic diseases; measurement of sensitivity and specificity in tumor-tissue genomic analysis with mixed-cells; mechanism analysis of differentiation and proliferation of cancer stem cell for academic purpose. Our single- cell RNA-seq technique shows that fragments were 250-450 bp after fragmentation, amplification, and adapter addition. There were 11.6 million reads mapped in raw sequencing reads (19.6 million). The numbers of mapped genes, mapped transcripts, and mapped exons were 31,332, 41,210, and 85,786, respectively. All QC results demonstrated that RNA-seq techniques could be used for single-cell genomic performance. Analysis of the mapped genes showed that the number of genes mapped by RNA-seq (6767 genes) was much higher than that of differential display (288 libraries) among similar specimens which we have developed and published. The single-cell RNA-seq can detect gene splicing using different subtype TGF-beta analysis. The results from using Q-rtPCR tests demonstrated that sensitivity is 76% and specificity is 55% from single-cell RNA-seq technique with some gene expression missing (2/8 genes). However, it will be feasible to use RNA-seq techniques to contribute to genomic medicine at single-cell level.

Single-cell responses to ionizing radiation

While gene expression studies have proved extremely important in understanding cellular processes, it is becoming more apparent that there may be differences in individual cells that are missed by studying the population as a whole. We have developed a qRT-PCR protocol that allows us to assay multiple gene products in small samples, starting at 100 cells and going down to a single cell, and have used it to study radiation responses at the single-cell level. Since the accuracy of qRT-PCR depends greatly on the choice of "housekeeping" genes used for normalization, initial studies concentrated on determining the optimal panel of such genes. Using an endogenous control array, it was found that for IMR90 cells, common housekeeping genes tend to fall into one of two categories-those that are relatively stably expressed regardless of the number of cells in the sample, e.g., B2M, PPIA, and GAPDH, and those that are more variable (again regardless of the size of the population), e.g., YWHAZ, 18S, TBP, and HPRT1. Further, expression levels in commonly studied radiation-response genes, such as ATF3, CDKN1A, GADD45A, and MDM2, were assayed in 100, 10, and single-cell samples. It is here that the value of single-cell analyses becomes apparent. It was observed that the expression of some genes such as FGF2 and MDM2 was relatively constant over all irradiated cells, while that of others such as FAS was considerably more variable. It was clear that almost all cells respond to ionizing radiation but the individual responses were considerably varied. The analyses of single cells indicate that responses in individual cells are not uniform and suggest that responses observed in populations are not indicative of identical patterns in all cells. This in turn points to the value of single-cell analyses.

Clinical Genomic Analysis and Diagnosis --Genomic Analysis Ex Vivo, in Vitro and in Silico

Seven years ago, I systemically reviewed single cell techniques with genomic and proteomic analyses which was called Single-Cell Genomic Analysis. After many years of arduous work, single cell techniques with downstream genomic and proteomic analysis have been applied to clinical fields including molecular pathology, molecular genetics, forensic medicine and biomarker discovery. On top of that, dynamic cell-sorting technique combined with downstream cell culture and genomic analysis of stem cell for regeneration medicine and cancer stem cell for differentiation have also been greatly developed in clinical fields. More importantly, tissue level sampling with in silico analysis has been applied in therapeutic targeting for advanced neoplastic disease. Recent development in sorting homogeneous cells in vitro (or single cells technique), ex vivo (dynamic analysis or small number of cell culture with downstream genomic analysis) and insilico (tissue level sampling with in silico analysis) have allowed physician scientists with a choice to select one of these above techniques with genomic analysis to apply to their clinical research fields. To fully understand these modern techniques, this manual will review recently developed methods or clinical genomic analysis in vitro, in silico and ex vivo. In the review paper, I will also introduce how to utilize these techniques in different clinical fields. The manual will also address some of the challenges for clinical genomics analysis and diagnosis due to mixed cells from clinical specimens.

Analysis of gene expression at the single-cell level using microdroplet-based microfluidic technology

In the present work, we have measured the messenger RNA expression of specific genes both from total RNA and cells encapsulated in droplets. The microfluidic chip introduced includes the following functionalities: RNA∕cell encapsulation, lysis, reverse transcription and real-time polymerase chain reaction. We have shown that simplex and duplex gene expression measurements can be carried out over a population of 100 purified RNA samples encapsulated simultaneously in 2 nl droplets in less than 2 h. An analysis of 100 samples containing one to three cells has shown excellent consistency with standard techniques regarding average values. The cell-to-cell distributions of the E-cadherin expression suggest fluctuations on the order of 80% in the number of transcripts, which is highly consistent with the general findings from the literature. A mathematical model has also been introduced to strengthen the interpretation of our results. The present work paves the way for the systematic acquisition of such information in biological and biomedical studies.