组织单腺体内单细胞的基因变异分析方法

从单细胞尺度进行细胞异质性的分析是深度理解细胞群体关系的关键。组织中的单细胞由于细胞类型不同,尺寸往往相差很大,但是目前常用的基于微孔板和Fluidigm公司的微流控的单细胞组学研究方法,需要入口的单细胞大小相近。本研究以胃组织为例,建立了一种组织单细胞的基因变异分析方法,实现了尺寸差异较大的单细胞的基因变异分析。在该方法中,先将胃组织裂解获得单个腺体,再将单个腺体酶解得到不同大小的腺体内单细胞,然后把这些单细胞铺在聚乙烯萘膜载玻片上,进行激光显微切割分选、全基因组放大,最后测其微卫星的长度。利用该方法,成功在肠上皮化生腺体内部检测到微卫星长度的变化,并灵活地对尺寸差异大的组织细胞以及肠化生腺体细胞进行了精细分析。此外,这种单细胞分析方法还可以对带有不同标记的细胞进行低通量和高通量的基因组分析,为单细胞尺度上的组织异质性研究提供了一种高度灵活的分析方法。     

Deep Profiling of Cellular Heterogeneity by Emerging Single‐Cell Proteomic Technologies

The ability to comprehensively profile cellular heterogeneity in functional proteome is crucial in advancing the understanding of cell behavior, organism development, and disease mechanisms. Conventional bulk measurement by averaging the biological responses across a population often loses the information of cellular variations. Single‐cell proteomic technologies are becoming increasingly important to understand and discern cellular heterogeneity. The well‐established methods for single‐cell protein analysis based on flow cytometry and fluorescence microscopy are limited by the low multiplexing ability owing to the spectra overlap of fluorophores for labeling antibodies. Recent advances in mass spectrometry (MS), microchip, and reiterative staining‐based techniques for single‐cell proteomics have enabled the evaluation of cellular heterogeneity with high throughput, increased multiplexity, and improved sensitivity. In this review, the principles, developments, advantages, and limitations of these advanced technologies in analysis of single‐cell proteins, along with their biological applications to study cellular heterogeneity, are described. At last, the remaining challenges, possible strategies, and future opportunities that will facilitate the improvement and broad applications of single‐cell proteomic technologies in cell biology and medical research are discussed.     

Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry

Laser capture microdissection (LCM) is a powerful tool that enables the isolation of specific cell types from tissue sections, overcoming the problem of tissue heterogeneity and contamination. This study combined the LCM with isotope-coded affinity tag (ICAT) technology and two-dimensional liquid chromatography to investigate the qualitative and quantitative proteomes of hepatocellular carcinoma (HCC). The effects of three different histochemical stains on tissue sections have been compared, and toluidine blue stain was proved as the most suitable stain for LCM followed by proteomic analysis. The solubilized proteins from microdissected HCC and non-HCC hepatocytes were qualitatively and quantitatively analyzed with two-dimensional liquid chromatography tandem mass spectrometry (2D-LC-MS/MS) alone or coupled with cleavable ICAT labeling technology. A total of 644 proteins were qualitative identified, and 261 proteins were unambiguously quantitated. These results show that the clinical proteomic method using LCM coupled with ICAT and 2D-LC-MS/MS can carry out not only large-scale but also accurate qualitative and quantitative analysis.     

Targeted tissue proteomic analysis of human astrocytomas

Complicating proteomic analysis of whole tissues is the obvious problem of cell heterogeneity in tissues, which often results in misleading or confusing molecular findings. Thus, the coupling of tissue microdissection for tumor cell enrichment with capillary isotachophoresis-based selective analyte concentration not only serves as a synergistic strategy to characterize low abundance proteins, but it can also be employed to conduct comparative proteomic studies of human astrocytomas. A set of fresh frozen brain biopsies were selectively microdissected to provide an enriched, high quality, and reproducible sample of tumor cells. Despite sharing many common proteins, there are significant differences in the protein expression level among different grades of astrocytomas. A large number of proteins, such as plasma membrane proteins EGFR and Erbb2, are up-regulated in glioblastoma. Besides facilitating the prioritization of follow-on biomarker selection and validation, comparative proteomics involving measurements in changes of pathways are expected to reveal the molecular relationships among different pathological grades of gliomas and potential molecular mechanisms that drive gliomagenesis.     

Quantitative mRNA expression analysis in kidney glomeruli using microdissection techniques

The introduction of new tools for molecular analysis, such as RT-qPCR and microarrays, has provided researchers with powerful applications to study renal disease and development. However, the high cellular heterogeneity of the renal tissue complicates the molecular analysis of specific cells and cell groups such as glomerular or tubular cells. In the past, glomerular sieving and manual dissection were used for the isolation of glomeruli. However, these techniques cannot be used for the isolation of specific glomeruli or for the co-isolation of additional tissue fractions. In recent decades, new microdissection techniques such as laser-assisted microdissection have been developed. These applications allow the isolation of small cell groups from heterogeneous tissue for molecular analysis, including microarray and RT-qPCR. Although very promising, some drawbacks are associated with these techniques. The isolated sample material is generally small and requires sensitive assays. In addition, the long sample processing time may result in a considerable loss of RNA integrity. Careful optimization and rigorous quality analysis should overcome these drawbacks. In the present paper, the recent literature on the application of microdissection techniques in kidney research is reviewed, together with a discussion of the critical issues that are essential for the application of quantitative mRNA expression analysis with RT-qPCR on microdissected samples.     

The advantage of laser‐capture microdissection over whole tissue analysis in proteomic profiling studies

Laser‐capture microdissection (LCM) offers a reliable cell population enrichment tool and has been successfully coupled to MS analysis. Despite this, most proteomic studies employ whole tissue lysate (WTL) analysis in the discovery of disease biomarkers and in profiling analyses. Furthermore, the influence of tissue heterogeneity in WTL analysis, nor its impact in biomarker discovery studies have been completely elucidated. In order to address this, we compared previously obtained high resolution MS data from a cohort of 38 breast cancer tissues, of which both LCM enriched tumor epithelial cells and WTL samples were analyzed. Label‐free quantification (LFQ) analysis through MaxQuant software showed a significantly higher number of identified and quantified proteins in LCM enriched samples (3404) compared to WTLs (2837). Furthermore, WTL samples displayed a higher amount of missing data compared to LCM both at peptide and protein levels (p‐value < 0.001). 2D analysis on co‐expressed proteins revealed discrepant expression of immune system and lipid metabolisms related proteins between LCM and WTL samples. We hereby show that LCM better dissected the biology of breast tumor epithelial cells, possibly due to lower interference from surrounding tissues and highly abundant proteins. All data have been deposited in the ProteomeXchange with the dataset identifier PXD002381 ( http://proteomecentral.proteomexchange.org/dataset/PXD002381 ).     

PDMS Microwell Stencil Based Multiplexed Single‐Cell Secretion Analysis

Multiplexed single‐cell protein secretion analysis provides an in‐depth understanding of cellular heterogeneity in intercellular communications mediated by secreted proteins in both fundamental and clinical research. However, it has been challenging to increase the proteomic parameters co‐profiled from every single cell in a facile way. Herein, a simple method to improve the multiplexed proteomic parameters of PDMS microwell based single‐cell secretion analysis platform by sandwiching PDMS stencil in between two antibody‐coated glass slides is introduced. Two different antibody panels can be immobilized easily by static coating, without using sophisticated fluid handling or bulky equipment. 5‐plexed, 3‐fluorescence color single‐cell secretion assay is demonstrated with this platform to investigate human monocytic U937 cells in response to lipopolysaccharide and phorbol myristate acetate stimulation, which identified the existence of functional subsets dictated by different cytokine profiles. The technology introduced here is simple, easy to operate, which holds great potential to become a powerful tool for profiling multiplexed single‐cell cytokine secretion at high throughput to dissect cellular heterogeneity in secretome signatures.     

Cancer biomarker discovery and translation: proteomics and beyond

Introduction: Cancer is often diagnosed at late stages when the chance of cure is relatively low and although research initiatives in oncology discover many potential cancer biomarkers, few transition to clinical applications. This review addresses the current landscape of cancer biomarker discovery and translation with a focus on proteomics and beyond.

Areas covered: The review examines proteomic and genomic techniques for cancer biomarker detection and outlines advantages and challenges of integrating multiple omics approaches to achieve optimal sensitivity and address tumor heterogeneity. This discussion is based on a systematic literature review and direct participation in translational studies.

Expert commentary: Identifying aggressive cancers early on requires improved sensitivity and implementation of biomarkers representative of tumor heterogeneity. During the last decade of genomic and proteomic research, significant advancements have been made in next generation sequencing and mass spectrometry techniques. This in turn has led to a dramatic increase in identification of potential genomic and proteomic cancer biomarkers. However, limited successes have been shown with translation of these discoveries into clinical practice. We believe that the integration of these omics approaches is the most promising molecular tool for comprehensive cancer evaluation, early detection and transition to Precision Medicine in oncology.     

植物蛋白质组学研究进展

 蛋白质组学是后基因组时代功能基因组学研究的新兴学科和热点领域。该文简要介绍了蛋白质组学产生的科学背景、研究方法和研究内容。蛋白质组学研究方法主要有双向聚丙烯酰胺凝胶电泳（2D-PAGE）、质谱(Mass-spectrometric)技术、蛋白质芯片（Protein chips）技术、酵母双杂交系统（Yeast two-hybrid system）、植物蛋白质组数据库等。其应用的范围包括植物群体遗传学、在个体水平上植物对生物和非生物环境的适应机制、植物的发育和组织器官的分化过程,以及不同亚细胞结构在生理生态过程中的作用等诸多方面。同时对植物蛋白质组学的发展前景进行了展望。     

HTAPP: High‐throughput autonomous proteomic pipeline

Recent advances in the speed and sensitivity of mass spectrometers and in analytical methods, the exponential acceleration of computer processing speeds, and the availability of genomic databases from an array of species and protein information databases have led to a deluge of proteomic data. The development of a lab‐based automated proteomic software platform for the automated collection, processing, storage, and visualization of expansive proteomic data sets is critically important. The high‐throughput autonomous proteomic pipeline described here is designed from the ground up to provide critically important flexibility for diverse proteomic workflows and to streamline the total analysis of a complex proteomic sample. This tool is composed of a software that controls the acquisition of mass spectral data along with automation of post‐acquisition tasks such as peptide quantification, clustered MS/MS spectral database searching, statistical validation, and data exploration within a user‐configurable lab‐based relational database. The software design of high‐throughput autonomous proteomic pipeline focuses on accommodating diverse workflows and providing missing software functionality to a wide range of proteomic researchers to accelerate the extraction of biological meaning from immense proteomic data sets. Although individual software modules in our integrated technology platform may have some similarities to existing tools, the true novelty of the approach described here is in the synergistic and flexible combination of these tools to provide an integrated and efficient analysis of proteomic samples.     

Detection and identification of heat shock protein 10 as a biomarker in colorectal cancer by protein profiling

Although colorectal cancer is one of the best-characterized tumors with regard to the multistep progression, it remains one of the most frequent and deadly neoplasms. For a better understanding of the molecular mechanisms behind the process of tumorigenesis and tumor progression, changes in protein expression between microdissected normal and tumorous colonic epithelium were analyzed. Cryostat sections from colorectal tumors, adenoma tissue, and adjacent normal mucosa were laser-microdissected and analyzed using ProteinChip Arrays. The derived MS profiles exhibited numerous statistical differences. One peak showing significantly high expression in the tumor was purified by reverse-phase chromatography and SDS-PAGE. The protein band of interest was passively eluted from the gel and identified as heat shock protein 10 (HSP 10) by tryptic digestion, peptide mapping, and MS/MS analysis. This tumor marker was further characterized by immunohistochemistry. Analysis of HSP 10-positive tissue by ProteinChip technology confirmed the identity of this protein. This work demonstrates that biomarker in colorectal cancer can be detected, identified, and assessed by a proteomic approach comprising tissue microdissection, protein profiling, and immunological techniques. In our experience, histological defined microdissected tissue areas should be used to identify proteins that might be responsible for tumorigenesis.     

Total variance should drive data handling strategies in third generation proteomic studies

Quantitative proteomics is entering its “third generation,” where intricate experimental designs aim to increase the spatial and temporal resolution of protein changes. This paper re‐analyses multiple internally consistent proteomic datasets generated from whole cell homogenates and fractionated brain tissue samples providing a unique opportunity to explore the different factors influencing experimental outcomes. The results clearly indicate that improvements in data handling are required to compensate for the increased mean CV associated with complex study design and intricate upstream tissue processing. Furthermore, applying arbitrary inclusion thresholds such as fold change in protein abundance between groups can lead to unnecessary exclusion of important and biologically relevant data.     

High-resolution array CGH increases heterogeneity tolerance in the analysis of clinical samples

Recent advances in array comparative genomic hybridization (array CGH) technology are revolutionizing our understanding of tumor genomes. Marker-based arrays enable rapid survey at megabase intervals, while tiling path arrays examine the entire genome in unprecedented detail. Tumor biopsies are typically small and contain infiltrating stromal cells, requiring tedious microdissection. Tissue heterogeneity is a major barrier to high-throughput profiling of tumor genomes and is also an important consideration for the introduction of array CGH to clinical settings. We propose that increasing array resolution will enhance detection sensitivity in mixed tissues and as a result significantly reduce microdissection requirements. In this study, we first simulated normal cell contamination to determine the heterogeneity tolerance of array CGH and then validated this detection sensitivity model on cancer specimens using the newly developed submegabase resolution tiling-set (SMRT) array, which spans the human genome with 32,433 overlapping BAC clones.     

Compatibility of toluidine blue with laser microdissection and saturation labeling DIGE

Tissue fixation and staining protocols for laser microdissection are frequently not fully compatible with subsequent proteomic analysis. We compared the effect of three common histological stains (toluidine blue (TB), hemotoxylin, and hematoxylin and eosin (HE)) on tissue visualization, protein recovery, the saturation labeling reaction, and 2‐D electrophoresis. TB provided the best visualization of colorectal tumor tissue during laser microdissection (LMD) and had a comparable effect on protein recovery and the saturation labeling reaction with hematoxylin, provided a modified 2‐D clean‐up protocol was used. Eosin inhibited both protein recovery and the saturation labeling reaction.     

Proteomic signatures for histological types of lung cancer

We performed proteomic studies on lung cancer cells to elucidate the mechanisms that determine histological phenotype. Thirty lung cancer cell lines with three different histological backgrounds (squamous cell carcinoma, small cell lung carcinoma and adenocarcinoma) were subjected to two-dimensional difference gel electrophoresis (2-D DIGE) and grouped by multivariate analyses on the basis of their protein expression profiles. 2-D DIGE achieves more accurate quantification of protein expression by using highly sensitive fluorescence dyes to label the cysteine residues of proteins prior to two-dimensional polyacrylamide gel electrophoresis. We found that hierarchical clustering analysis and principal component analysis divided the cell lines according to their original histology. Spot ranking analysis using a support vector machine algorithm and unsupervised classification methods identified 32 protein spots essential for the classification. The proteins corresponding to the spots were identified by mass spectrometry. Next, lung cancer cells isolated from tumor tissue by laser microdissection were classified on the basis of the expression pattern of these 32 protein spots. Based on the expression profile of the 32 spots, the isolated cancer cells were categorized into three histological groups: the squamous cell carcinoma group, the adenocarcinoma group, and a group of carcinomas with other histological types. In conclusion, our results demonstrate the utility of quantitative proteomic analysis for molecular diagnosis and classification of lung cancer cells.