Large-scale protein profiling in human cell lines using antibody-based proteomics

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

MicroPOTS Analysis of Barrett's Esophageal Cell Line Models Identifies Proteomic Changes after Physiologic and Radiation Stress

Moving from macroscale preparative systems in proteomics to micro- and nanotechnologies offers researchers the ability to deeply profile smaller numbers of cells that are more likely to be encountered in clinical settings. Herein a recently developed microscale proteomic method, microdroplet processing in one pot for trace samples (microPOTS), was employed to identify proteomic changes in ∼200 Barrett’s esophageal cells following physiologic and radiation stress exposure. From this small population of cells, microPOTS confidently identified >1500 protein groups, and achieved a high reproducibility with a Pearson’s correlation coefficient value of R > 0.9 and over 50% protein overlap from replicates. A Barrett’s cell line model treated with either lithocholic acid (LCA) or X-ray had 21 (e.g., ASNS, RALY, FAM120A, UBE2M, IDH1, ESD) and 32 (e.g., GLUL, CALU, SH3BGRL3, S100A9, FKBP3, AGR2) overexpressed proteins, respectively, compared to the untreated set. These results demonstrate the ability of microPOTS to routinely identify and quantify differentially expressed proteins from limited numbers of cells.

A stromal cell population in the large intestine identified by tissue factor expression that is lost during colorectal cancer progression

Colorectal cancer (CRC) is a major cause of morbidity and mortality, and the composition of the tumour stroma is a strong predictor of survival in this cancer type. Tissue factor (TF) functions as the trigger of haemostasis together with its ligand coagulation factor VII/VIIa, and TF expression has been found in tumour cells of colorectal tumours. However, TF expression in the CRC tumour stroma or its relationship to patient outcome has not yet been studied. To address this question we developed and validated a specific anti-TF antibody using standardised methods within the Human Protein Atlas project. We used this antibody to investigate TF expression in normal colorectal tissue and CRC using immunofluorescence and immunohistochemistry in two patient cohorts. TF was strongly expressed in a cell population immediately adjacent to the colorectal epithelium. These TF-positive cells were ACTA2-negative but weakly vimentin-positive, defining a specific population of pericryptal sheath cells. In colorectal tumours, TF-positive sheath cells were progressively lost after the adenoma-to-carcinoma transition, demonstrating downregulation of this source of TF in CRC. Furthermore, loss of sheath cell TF was significantly associated with poor overall and disease-specific survival in rectal but not colon cancers. In conclusion, we demonstrate that TF is a marker of a specific cell population in the large intestine, which is lost during CRC progression. Our results highlight the role of the tumour stroma in this cancer type and suggest TF to be a potential prognostic biomarker in rectal cancers through the identification of pericryptal sheath cells.

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Sub-cellular localisation of proteins is critical to their function in all cellular processes; proteins localising to their intended micro-environment, e.g organelles, vesicles or macro-molecular complexes, will meet the interaction partners and biochemical conditions suitable to pursue their molecular function. Therefore, sound data and methods to reliably and systematically study protein localisation, and hence their mis-localisation and the disruption of protein trafficking, that are relied upon by the cell biology community, are essential. Here we present a method to infer protein localisation relying on the optimal integration of experimental mass spectrometry-based data and auxiliary sources, such as GO annotation, outputs from third-party software, protein-protein interactions or immunocytochemistry data. We found that the application of transfer learning algorithms across these diverse data sources considerably improves on the quantity and reliability of sub-cellular protein assignment, compared to single data classifiers previously applied to infer sub-cellular localisation using experimental data only. We show how our method does not compromise biologically relevant experimental-specific signal after integration with heterogeneous freely available third-party resources. The integration of different data sources is an important challenge in the data intensive world of biology and we anticipate the transfer learning methods presented here will prove useful to many areas of biology, to unify data obtained from different but complimentary sources.

Integrating phenotypic small-molecule profiling and human genetics: the next phase in drug discovery

Over the past decade, tremendous progress in high-throughput small molecule-screening methods has facilitated the rapid expansion of phenotype-based data. Parallel advances in genomic characterization methods have complemented these efforts by providing a growing list of annotated cell line features. Together, these developments have paved the way for feature-based identification of novel, exploitable cellular dependencies, subsequently expanding our therapeutic toolkit in cancer and other diseases. Here, we provide an overview of the evolution of phenotypic small-molecule profiling and discuss the most significant and recent profiling and analytical efforts, their impact on the field, and their clinical ramifications. We additionally provide a perspective for future developments in phenotypic profiling efforts guided by genomic science.

Targeted development of specific biomarkers of endometrial stromal cell differentiation using bioinformatics: the IFITM1 model

When classifying cellular uterine mesenchymal neoplasms, histological distinction of endometrial stromal from smooth muscle neoplasms can be difficult. The only widely established marker of endometrial stromal differentiation, CD10, has marginal specificity. We took a bioinformatics approach to identify more specific markers of endometrial stromal differentiation by searching the Human Protein Atlas, a public database of protein expression profiles. After screening the database using different methods, interferon-induced transmembrane protein 1 (IFITM1) was selected for further analysis. Immunohistochemistry for IFITM1 was performed using tissue sections from the selected cases of proliferative endometrium (22), secretory endometrium (6), inactive endometrium (19), adenomyosis (10), conventional leiomyoma (11), cellular leiomyoma (16), endometrial stromal nodule (2), low-grade endometrial stromal sarcoma (16), high-grade endometrial stromal sarcoma (2) and undifferentiated uterine sarcoma (2). Stained slides were scored in terms of intensity and distribution. Normal endometrial samples uniformly showed diffuse and strong IFITM1 staining. Endometrial stromal neoplasms, particularly low-grade endometrial stromal sarcoma, showed higher IFITM1 expression compared with smooth muscle neoplasms (P<0.0001). IFITM1 immunohistochemistry has high sensitivity and specificity, particularly in the distinction between low-grade endometrial stromal sarcoma and leiomyoma (81.2 and 86.7%, respectively). Our results indicate that IFITM1 is a sensitive and specific marker of endometrial stromal differentiation across the spectrum from proliferative endometrium to metastatic stromal sarcoma. IFITM1 is a potential valuable addition to immunohistochemical panels used in the diagnosis of cellular mesenchymal uterine tumors. Further studies with larger number of cases are necessary to corroborate this impression and determine the utility of IFITM1 in routine practice. This study is a clear example of how bioinformatics, particularly tools for mining genomic and proteomic databases, can enhance and accelerate biomarker development in diagnostic pathology.

Label-free quantitative proteomics trends for protein-protein interactions

Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.

The bioinformatics tool box for reproductive biology

Genetics and molecular biology have been instrumental for a better understanding of heritable defects causing human infertility over the past decades. More recently, the field of reproductive biology has harnessed genome biological approaches to gain insight into molecular processes underlying normal and pathological gametogenesis and gamete function. We are currently witnessing yet another quantum leap in our ability to monitor the flow of information from the genome via the transcriptome to the proteome: tiling arrays that cover both strands of a given target genome and RNA-Seq, a method based on ultra-high throughput DNA sequencing, enable us to study noncoding and protein-coding transcripts with unprecedented precision and depth at a reasonable cost. These technologies have spawned a thriving discipline within the bioinformatics field that employs information technology for managing and interpreting biological high-throughput data. This review outlines database projects and online analysis tools useful for life scientists in general and discusses in detail selected projects that have specifically been developed for researchers and clinicians in the field of reproductive biology. This article is part of a Special Issue entitled: Molecular Genetics of Human Reproductive Failure.

Mass spectrometry analysis and quantitation of peptides presented on the MHC II molecules of mouse spleen dendritic cells

Major histocompatibility complex class II (MHC II) molecules are expressed on the surface of antigen-presenting cells and display short bound peptide fragments derived from self- and nonself antigens. These peptide-MHC complexes function to maintain immunological tolerance in the case of self-antigens and initiate the CD4(+) T cell response in the case of foreign proteins. Here we report the application of LC-MS/MS analysis to identify MHC II peptides derived from endogenous proteins expressed in freshly isolated murine splenic DCs. The cell number was enriched in vivo upon treatment with Flt3L-B16 melanoma cells. In a typical experiment, starting with about 5 × 10(8) splenic DCs, we were able to reliably identify a repertoire of over 100 MHC II peptides originating from about 55 proteins localized in membrane (23%), intracellular (26%), endolysosomal (12%), nuclear (14%), and extracellular (25%) compartments. Using synthetic isotopically labeled peptides corresponding to the sequences of representative bound MHC II peptides, we quantified by LC-MS relative peptide abundance. In a single experiment, peptides were detected in a wide concentration range spanning from 2.5 fmol/μL to 12 pmol/μL or from approximately 13 to 2 × 10(5) copies per DC. These peptides were found in similar amounts on B cells where we detected about 80 peptides originating from 55 proteins distributed homogenously within the same cellular compartments as in DCs. About 90 different binding motifs predicted by the epitope prediction algorithm were found within the sequences of the identified MHC II peptides. These results set a foundation for future studies to quantitatively investigate the MHC II repertoire on DCs generated under different immunization conditions.

The Human Protein Atlas as a proteomic resource for biomarker discovery

The analysis of tissue-specific expression at both the gene and protein levels is vital for understanding human biology and disease. Antibody-based proteomics provides a strategy for the systematic generation of antibodies against all human proteins to combine with protein profiling in tissues and cells using tissue microarrays, immunohistochemistry and immunofluorescence. The Human Protein Atlas project was launched in 2003 with the aim of creating a map of protein expression patterns in normal cells, tissues and cancer. At present, 11,200 unique proteins corresponding to over 50% of all human protein-encoding genes have been analysed. All protein expression data, including underlying high-resolution images, are published on the free and publically available Human Protein Atlas portal (http://www.proteinatlas.org). This database provides an important source of information for numerous biomedical research projects, including biomarker discovery efforts. Moreover, the global analysis of how our genome is expressed at the protein level has provided basic knowledge on the ubiquitous expression of a large proportion of our proteins and revealed the paucity of cell- and tissue-type-specific proteins.