Figure 3 - uploaded by Biaoru Li
Content may be subject to copyright.
A strategy of genome library from single cells. 

A strategy of genome library from single cells. 

Source publication
Article
Full-text available
Recent advances in functional genomics allow us to estimate the expression of several thousands of genes in the mammalian genome. Techniques such as microarrays, expressed tag sequencing (EST), serial analysis of gene expression (SAGE), subtractive cloning and differential display (DD), and two-dimensional electrophoresis gel have been extensively...

Contexts in source publication

Context 1
... should be applied in single-cell studies. At present, there are two strategies to employ genome information amplification: mRNA amplification (aRNA) and PCR-based cDNA amplification. The aRNA procedure begins with total RNA or poly(A) RNA that is reverse transcribed using an oligo (dT) primer containing a T7 RNA polymerase promoter sequence. After first-strand synthesis, the reaction is treated with RNase H to fragment the mRNA. These fragments serve as primers during a second-strand synthesis reaction that produces a double-stranded DNA template for transcription. rRNA, mRNA fragments and primers are removed before using the cDNA template to produce linearly amplified aRNA. The amplification yields can reach 1000-5000 fold following two rounds of in vitro transcription (Figure 2-I). RNA amplification and is commercially available and has been increasingly reported in gene expression studies (Eberwine, 1996). PCR-based amplification has two protocols: specific profile and global profile applications. Specific profile methods such as rtPCR or multiplex rtPCR reactions are sensitive at the single-cell level, especially in nested PCR. Because the genes studied this way are pre-selected, it can only be applied to known genes. In order to overcome the problem, global PCR-based approaches have been developed in genomic analysis. One approach is homomeric tailings, and another is 3 ́-(3-primer-end) amplification (TPEA). The former (Figure 2-II) (Toellner et al. 1996) uses terminal deoxynucleotide transferase-generated homomeric 3’ tails to the first-strand cDNA. After rtPCR and 3’ tailing addition and PCR amplification, it has been applied to the analysis of single-cell global gene expression. Even though homomeric tailings can be used effectively in global profile analysis, many of the cDNA copies are not full length and shorter cDNAs are preferentially amplified. 3-primer-end- amplification (TPEA, Figure 2-III) (Dixon et al. 1998) is a partially randomized amplification of mRNA using an oligo-dT primer together with a 5’ primer containing a random pentamer. It can enable the detection of both high- and low-abundance mRNA transcripts from single cells. Because TPEA also has a 3’ bias, full-length cloning is more difficult. Moreover, only 40 to 50 genes can be analyzed in samples derived from single cells. We have also developed a more facile strategy to screen the genome at the single cell level. To illustrate, three techniques (RNA directly from cell lysis, randomized primer design as differential display and single cell genome cloned into plasmids) are simultaneously combined (Li et al. 2000). Following cell lysis and reverse transcription PCR, a 3’ end oligo (dT) n primer and a set of 5’ end arbitrary primers (both containing restriction enzyme terminals) are used in an amplification by PCR. After double digestion, the genome from a small number of cells is introduced into plasmids and transformed into cells. As Figure 2 and Figure 3 indicate, subtractive hybridization from a reference cell genome is employed in the modified method so that artefacts of cDNA amplification from test cells are minimized (Li et al. 2002). The technique has been used in genome expression analysis from 10 to 100 cells. The advantage is that the expression results are very sensitive and accurate because they exclude problems of artefacts. The disadvantage is similar to TPEA, that is, expression has a 3’ bias (Figure 2-IV). Over the past two years, as aRNA techniques have been developed, cDNA detection sensitivity has significantly increased so that we can use either cells from microdissection or single cells obtained from culture to analyze their genome expression (Figure 3) (Zhang et al. 2004). Protein signal amplification from a single-cell . Traditional methodologies for protein detection and quantification include two-dimensional gel electrophoresis, mass spectrometry, and antibody (Ab) binding. As we discussed previously, the application of these traditional proteomics-oriented technologies at the single-cell level has been limited because each methodology needs relatively large amounts of tissue. The development of protein arrays using antibody-binding technology has presented a new opportunity to study protein expression at the single cell level. Recently, two protein array techniques have rapidly been extended. The first is the use of intact antibodies, antibody fragments (single-chain fragment variable (ScFv) fragments) or exocyclic peptide-based complementarity determining region (CDR) subunits as antigen detectors. The second is the Ab used as amplification signals. In order to detect protein and peptide molecules, several Ab signal amplifications have been successfully employed in order to improve sensitivity. For example, immuno-PCR and T7 RNA amplification have been reported in several journals. In the former, the PCR technology is combined ...
Context 2
... should be applied in single-cell studies. At present, there are two strategies to employ genome information amplification: mRNA amplification (aRNA) and PCR-based cDNA amplification. The aRNA procedure begins with total RNA or poly(A) RNA that is reverse transcribed using an oligo (dT) primer containing a T7 RNA polymerase promoter sequence. After first-strand synthesis, the reaction is treated with RNase H to fragment the mRNA. These fragments serve as primers during a second-strand synthesis reaction that produces a double-stranded DNA template for transcription. rRNA, mRNA fragments and primers are removed before using the cDNA template to produce linearly amplified aRNA. The amplification yields can reach 1000-5000 fold following two rounds of in vitro transcription (Figure 2-I). RNA amplification and is commercially available and has been increasingly reported in gene expression studies (Eberwine, 1996). PCR-based amplification has two protocols: specific profile and global profile applications. Specific profile methods such as rtPCR or multiplex rtPCR reactions are sensitive at the single-cell level, especially in nested PCR. Because the genes studied this way are pre-selected, it can only be applied to known genes. In order to overcome the problem, global PCR-based approaches have been developed in genomic analysis. One approach is homomeric tailings, and another is 3 ́-(3-primer-end) amplification (TPEA). The former (Figure 2-II) (Toellner et al. 1996) uses terminal deoxynucleotide transferase-generated homomeric 3’ tails to the first-strand cDNA. After rtPCR and 3’ tailing addition and PCR amplification, it has been applied to the analysis of single-cell global gene expression. Even though homomeric tailings can be used effectively in global profile analysis, many of the cDNA copies are not full length and shorter cDNAs are preferentially amplified. 3-primer-end- amplification (TPEA, Figure 2-III) (Dixon et al. 1998) is a partially randomized amplification of mRNA using an oligo-dT primer together with a 5’ primer containing a random pentamer. It can enable the detection of both high- and low-abundance mRNA transcripts from single cells. Because TPEA also has a 3’ bias, full-length cloning is more difficult. Moreover, only 40 to 50 genes can be analyzed in samples derived from single cells. We have also developed a more facile strategy to screen the genome at the single cell level. To illustrate, three techniques (RNA directly from cell lysis, randomized primer design as differential display and single cell genome cloned into plasmids) are simultaneously combined (Li et al. 2000). Following cell lysis and reverse transcription PCR, a 3’ end oligo (dT) n primer and a set of 5’ end arbitrary primers (both containing restriction enzyme terminals) are used in an amplification by PCR. After double digestion, the genome from a small number of cells is introduced into plasmids and transformed into cells. As Figure 2 and Figure 3 indicate, subtractive hybridization from a reference cell genome is employed in the modified method so that artefacts of cDNA amplification from test cells are minimized (Li et al. 2002). The technique has been used in genome expression analysis from 10 to 100 cells. The advantage is that the expression results are very sensitive and accurate because they exclude problems of artefacts. The disadvantage is similar to TPEA, that is, expression has a 3’ bias (Figure 2-IV). Over the past two years, as aRNA techniques have been developed, cDNA detection sensitivity has significantly increased so that we can use either cells from microdissection or single cells obtained from culture to analyze their genome expression (Figure 3) (Zhang et al. 2004). Protein signal amplification from a single-cell . Traditional methodologies for protein detection and quantification include two-dimensional gel electrophoresis, mass spectrometry, and antibody (Ab) binding. As we discussed previously, the application of these traditional proteomics-oriented technologies at the single-cell level has been limited because each methodology needs relatively large amounts of tissue. The development of protein arrays using antibody-binding technology has presented a new opportunity to study protein expression at the single cell level. Recently, two protein array techniques have rapidly been extended. The first is the use of intact antibodies, antibody fragments (single-chain fragment variable (ScFv) fragments) or exocyclic peptide-based complementarity determining region (CDR) subunits as antigen detectors. The second is the Ab used as amplification signals. In order to detect protein and peptide molecules, several Ab signal amplifications have been successfully employed in order to improve sensitivity. For example, immuno-PCR and T7 RNA amplification have been reported in several journals. In the former, the PCR technology is combined ...
Context 3
... immuno-detection methods as shown in Figure 4(A) (McKie et al. 2002). Streptavidin is added to a biotinylated Ab-antigen complex whereupon a known biotinylated-DNA fragment is added, resulting in the formation of a specific antigen-Ab-DNA conjugate. The attached marker DNA can be amplified by PCR with appropriate primers. Some results have shown that ~10 5 fold increase in sensitivity over an alkaline phosphatase- conjugated ELISA is obtained. This approach, with slight modifications, has been used to detect a variety of antigens, including a human protooncogene protein and tumour necrosis factor α . Although the immuno-PCR technique has some advantages over traditional methods of protein detection, such as increase in sensitivity, there still exist several notable limitations to its use. One of the major limitations of immuno-PCR lies in the nonlinear amplification ability of PCR, which limits this technique as a quantitative detection method. Some of these problems have been overcome with a relatively isothermal rolling circle DNA amplification technique (RCA). As demonstrated in Figure 4(B) (Zhang et al. 2001), T7 RNA amplification resolves these problems and shows a linear relationship between protein expression and an expression indicator such as luciferase. Single-cell gene expression analysis can be carried out both at the specific profile and global genome profile. In situ hybridization and rtPCR belong to the specific profile. In situ PCR combined with immunohistochemical detection is frequently used as a measurement of single-cell gene activity (Gey et al. 1999). Multiplex rtPCR is also effective for observing gene expression at the single-cell level (Hahn et al. 2002). At present, rtPCR using real-time detection of PCR products can quantify gene expression at the single- cell level with reduced risk for artefacts resulting from contamination or illegitimate transcript amplification (Liss, 2002). However, because the primers are pre-selected, expression profiles will not contain previously unreported transcripts or novel sequences. Global genome profile expression analysis at the single-cell level holds new promise to analyze disease pathogenesis and tumorigenesis. At present, four techniques are utilized to advance the global genome profile of a single cell (in addition to the previously described specific profiles such as in situ hybridization and multiplex rtPCR). These global genome profiles include differential display, subtractive cloning, microarray, and protein array. As shown in Figure 2 and Figure 3, differential display and subtractive cloning can be employed with a small number of cells in which the resolution is from one cell to 10 4 cells (Chen and Talmage, 1999). Because both of these methods may have an artefact contamination after amplification, it may result in variable genome expression at the single-cell level. As discussed above, we introduced a strategy combining amplifying RNA, randomized primers (with restriction terminals for cloning into plasmid) and subtractive hybridization (for eliminating some artefacts), which has successfully been used in genome expression at single-cell level. Although our method still has a problem in screening the genome, that is, some 3’ bias, after sequencing the genome at 3’ terminal fragment, GenBank analysis can allow us to eventually determine full-length genome prediction (Li et al. 2000; Li et al. 2002; Zhang et al. 2004). Here, some explanation and details shown as above (such as including A: designs of random primers with restriction enzyme site, B: cDNA amplification, C: cloning into plasmid and storage of library and D: subtractive hybridization using reference cell cDNA), we illustrate the basic protocol ...

Citations

... In earlier studies, as mentioned above, we found that the efficacy of TILs in treating solid tumors varies from person to person. Then we used several strategies to address the issues shown in Fig-3 [4,26,[52][53][54][55][56][57][58][59][60][61] . In that period, we have used three ways to resolve these issues: removing inhibitory factors during TIL culture and reducing T cell quiescence; studying increasing the immune response to tumor cells such as using TNF-α retroviral vectors transduced into TILs; Collagenase IV under mild digestion conditions maintains TIL integrity to increase TIL exposure to tumor cells. ...
Article
Full-text available
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.
... To elucidate TIL quiescence from solid tumors, we screened and harvested quiescent TILs from those of hepatic cell cancers (HCCs). Before the genomic era (1996-2004), we used a single-cell messenger ribonucleic acid (mRNA) display system to find a profile from the quiescent cluster of differentiation 8 (CD8) TILs (43)(44)(45). After that, because microarray and ribonucleic acid sequencing (RNA-seq) began to be applied for single-cell genomic analyses, we carried out further studies of the set of quiescent genes by single-cell RNA-seq, as mentioned below. ...
Article
Full-text available
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.
... As a consequence, numerous studies have proposed integrated methods to perform single-cell -omic analyses. [5][6][7][8][9][10][11] Among commercially available products, one could mention the ICell8 12 (Takara, Kusatsu, Japan), the Polaris 13 (Fluidigm, South San Francisco, CA), and the Rhapsody 14 (BD, San Jose, CA). However, these solutions present a limitation when the cell content analysis is not sufficient for characterization, and the single cell has to be retrieved for further processing. ...
Article
Full-text available
Many biological methods are based on single-cell isolation. In single-cell line development, the gold standard involves the dilution of cells by means of a pipet. This process is time-consuming as it is repeated over several weeks to ensure clonality. Here, we report the modeling, designing, and testing of a disposable pipet tip integrating a cell sensor based on the Coulter principle. We investigate, test, and discuss the effects of design parameters on the sensor performances with an analytical model. We also describe a system that enables the dispensing of single cells using an instrumented pipet coupled with the sensing tip. Most importantly, this system allows the recording of an impedance trace to be used as proof of single-cell isolation. We assess the performances of the system with beads and cells. Finally, we show that the electrical detection has no effect on cell viability
... In order to achieve a complete system model for personalized chemotherapy, I will first address questions about genomic expression analysis from mixed-cells tumor tissues. In clinical genomic analysis from our previous work and other laboratories' reports, several protocols have been applied for analysis of clinical genomic expression level [13,14]: clinical sampling in vitro for genomic analysis (genomic analysis from single-cell sampling), clinical sampling ex vivo for genomic analysis (genomic analysis from purifying/expanding primary cells from clinical samples) and direct clinical genomic analysis in silico (direct analysis of genomic expression level by using different bioinformatics model to achieve genomic data from given tumor cells at tumor tissue level). ...
Chapter
Clinically, personalized medicine also referred to as precision medicine is a new medical model to be directly tailored for the care of individual patients. It is often called "the right treatment for the right person at the right time." Most successful examples of personalized treatments require a rational clinical genomic analysis. Following Research and Development (R&D) of techniques and analysis of clinical genomic expression, genomic expression profile along with system modeling has been increasingly applied for personalized therapy. Now personalized chemotherapy, one of personalized therapy, has been brought forward to the field of cancer. According to protocol of personalized chemotherapy from tumor tissue sampling to clinical application in queue, I will introduce the entire process including clinical sampling, analyzing mRNA genomic expression level with its diagnosis, discovering gene expression signature by system modeling and uncovering sensitive drugs from drugbank for clinical application. At present, after next-generation sequencing is brought into the new field, system modeling related with drugs discovery will make great contribution for future personalized chemotherapy of tumor diseases.
... In order to achieve objective results of genetic diagnosis and genomics data analysis for personalized target prevention and treatment, we will first address a question about SNP and genomic analysis from clinical samples. In clinical genomic analysis from reported evidences, several sampling techniques have been applied for clinical SNP and genomic analysis [7,8] : clinical sampling in vitro (single-cell sampling for SNP and genomic analysis), clinical sampling ex vivo (purifying/expanding primary cells ex vivo from clinical samples for SNP and genomic analysis) and direct clinical SNP and genomic analysis in silico (SNP and genomic analysis using different bioinformatics model to observe SNP and genomic data from tissue level). ...
Article
Personalized medicine, one of special prevention and therapeutic strategies, is going to develop into clinical fields. Personalized medicine is directly tailored for physicians to prevent and care individual patient. It is often called as "the right treatment for the right person at the right time." Most successful examples of personalized prevention and treatments require a rational clinical genomic analysis. Following Research and Development (R&D) of clinical genomic techniques, here we introduce personalized targeted-therapy based on either detection of single nucleotide polymorphisms (SNP) or detection of universal single nucleotide variance (SNVs) at genomic level. According to clinical protocol of personalized targeted prevention and therapy, whole performance includes clinical sampling, SNP detection technology and diagnosis (universal and designed), SNP signature discovered by system modeling and sensitive targeted molecules/drugs uncovered by drug-banks with its confirmation. Now, after (A) simplified and designed SNP detection test is applied for clinical patients and (B) next generation sequencing for universal SNP detection is brought into the new field, both universal and designed SNP detection systems related with sensitive targeted molecules will make great contribution for further personalized prevention and treatment of tumor diseases, genetic diseases and uncured disease in nerve, endocrine and cardiovascular systems.
... CD8 cells obtained from TIL of liver cancers were isolated, and a cDNA library was generated as previously reported [16]. Briefly, single CD8+ cells from TIL were directly lysed in an 8 í µí¼‡L DNA digestion buffer with DNase I (Sigma). ...
Article
Full-text available
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.
Article
This paper suggests advices to researchers that are to start collection of biological samples to be used for biomarkers with particular reference to guidelines that may be of assistance.
Article
Full-text available
A complete set of DNA with its transcripts is defined as genome, which includes both the genes and the noncoding sequences of the DNA/RNA. After making advances in decoding different genomes across species, genomic techniques such as SNP microarrays and gene expression microarray have been synchronously developed to analyze the genomic functions. Now, scientists are able to take the study of genomics into deep consideration of biological evolution and mechanism of different diseases. However, there are still challenges with the genomic technology. Some tissues of human and animals, such as tumor tissues, contain multiple heterogeneous cells, making analysis extremely difficult. Additionally, some specimens have very few cells, such as circulating tumor cells. To fully study DNA genomic changes and its expression changes in cancer, single-cell genomic techniques have been broadly applied to fields such as cytogenomic diagnosis for specimens on glass slides, tumor cells in circulating blood, measurement of sensitivity and specificity of genomic analysis at tumor tissue level, mechanism of differentiation of cancer stem cell, etc. Recently, next-generation sequencing (NGS) has become an important tool in single-cell genomic analysis. Here, we systemically introduce single-cell NGS from single-cell sampling, single-cell NGS, and single-cell NGS-related bioinformatics into its application for tumor biology. This chapteralso describes some advantages of single-cell NGS and addresses some challenges of single-cell NGS for genomics analysis due to the specimen features.
Article
Single-cell gene expression analysis holds great promise for studying diverse biological systems, but methodology to process these precious samples in a reproducible, quantitative, and parallel fashion remains challenging. Here, we utilize microfluidics to isolate picogram and subpicogram mRNA templates, as well as to synthesize cDNA from these templates. We demonstrate single-cell mRNA isolation and cDNA synthesis, provide quantitative calibrations for each step in the process, and measure gene expression in individual cells. The techniques presented here form the foundation for highly parallel single-cell gene expression studies.
Article
The increasing use of microarray expression profiling to study the molecular biology of cancer and the cellular physiology of difficult-to-isolate cell types has led to a need for methods that accurately and precisely amplify small quantities of RNA. The purpose of this review is to provide an overview of the existing methods for transcriptome amplification and to define the parameters for comparing different amplification methods. The authors propose a standardized protocol for the assessment and evaluation of amplification methods, focusing on a new whole-transcriptome amplification kit, which amplifies total RNA into cDNA fragments. Reproducibility and reliability of the method were analyzed and discussed using both quantitative real-time PCR and a high-density oligonucleotide microarray platform.