Utilizing IMC or MIBI, this chapter details the conjugation and validation methods for antibodies, along with staining procedures and preliminary data collection on both human and mouse pancreatic adenocarcinoma samples. The protocols' goal is to enable the application of these intricate platforms, not limited to tissue-based tumor immunology investigations, but also extending to wider tissue-based oncology and immunology studies.
The development and physiology of specialized cell types are under the control of sophisticated signaling and transcriptional programs. The origins of human cancers, stemming from a variety of specialized cell types and developmental stages, are linked to genetic disruptions in these regulatory programs. Developing effective immunotherapies and identifying viable drug targets hinges on a thorough understanding of these multifaceted biological systems and their potential to initiate cancer. The pioneering integration of single-cell multi-omics technologies, which analyze transcriptional states, has been accompanied by the expression of cell-surface receptors. In this chapter, the computational framework SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network) is described, which links transcription factors to the expression of cell-surface proteins. To model gene expression, SPaRTAN integrates CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) data and cis-regulatory sites to simulate how transcription factors and cell-surface receptors interact. The SPaRTAN pipeline is showcased using CITE-seq data collected from peripheral blood mononuclear cells.
An important instrument for biological research is mass spectrometry (MS), as it uniquely allows for the examination of a broad collection of biomolecules, including proteins, drugs, and metabolites, beyond the scope of typical genomic platforms. Evaluating and integrating measurements across diverse molecular classes presents a significant complication for downstream data analysis, demanding expertise from a range of relevant fields. The sophisticated nature of this limitation hinders the regular application of multi-omic methods employing MS, despite the substantial biological and functional understanding derived from the data. 17a-Hydroxypregnenolone mw In response to this unmet need, our group developed Omics Notebook, an open-source platform that provides for automated, reproducible, and customizable analysis, reporting, and integration of MS-based multi-omic data. This pipeline's implementation delivers a framework that allows researchers to more efficiently pinpoint functional patterns across multiple data types, highlighting statistically significant and biologically pertinent information from their multi-omic profiling experiments. The chapter details a protocol, leveraging our accessible tools, to analyze and integrate high-throughput proteomics and metabolomics data, producing reports that enhance the impact of research, support collaborations across institutions, and facilitate a wider distribution of data.
Intracellular signal transduction, gene transcription, and metabolic processes all have protein-protein interactions (PPI) as their structural and functional underpinnings. Various diseases, including cancer, have PPI implicated in their pathogenesis and development. Using gene transfection and molecular detection technologies, researchers have meticulously analyzed the PPI phenomenon and their associated functions. Differently, in histopathological evaluations, despite immunohistochemical techniques revealing information about protein expression and their location within diseased tissues, the visualization of protein-protein interactions has remained difficult. A new in situ proximity ligation assay (PLA) was developed for the microscopic identification of protein-protein interactions (PPI) in specimens of formalin-fixed, paraffin-embedded tissue, cultured cells, and frozen tissue. Employing PLA on histopathological specimens enables thorough cohort studies of PPI, thus shedding light on PPI's impact on pathology. Employing breast cancer tissues preserved via FFPE, we have previously established the dimerization pattern of estrogen receptors and the significance of HER2-binding proteins. This chapter presents a methodology for the visualization of protein-protein interactions (PPIs) in pathological tissue samples employing photolithographically generated arrays (PLAs).
Clinically, nucleoside analogs (NAs) serve as a recognized class of anticancer drugs, employed in the treatment of various cancers, either as single-agent therapy or combined with other established anticancer or pharmacological treatments. Currently, an impressive number of almost a dozen anticancer nucleic acid drugs have been authorized by the FDA, and several innovative nucleic acid drugs are undergoing preclinical and clinical trials for possible future uses. brain pathologies A significant hurdle to treatment efficacy is the insufficient uptake of NAs by tumor cells, resulting from changes in the expression of drug carrier proteins (such as solute carrier (SLC) transporters) within the tumor cells and surrounding cells in the tumor microenvironment. Tissue microarrays (TMA) and multiplexed immunohistochemistry (IHC) enable a high-throughput analysis of alterations in numerous chemosensitivity determinants within hundreds of patient tumor tissues, representing a significant advancement over the conventional IHC approach. Employing a TMA from pancreatic cancer patients treated with gemcitabine, we outline a detailed protocol for multiplexed IHC analysis in this chapter. The procedure, optimized within our laboratory, encompasses slide imaging, marker quantification, and a discussion of experimental design and procedural considerations.
Inherent or treatment-induced resistance to anticancer drugs is a common side effect of cancer therapy. A deep understanding of how drugs lose their effectiveness can facilitate the design of new therapies. To ascertain pathways associated with drug resistance, drug-sensitive and drug-resistant variants are subjected to single-cell RNA sequencing (scRNA-seq), followed by network analysis of the scRNA-seq dataset. This protocol outlines a computational analysis pipeline for investigating drug resistance, employing the integrative network analysis tool PANDA on scRNA-seq expression data. PANDA incorporates protein-protein interactions (PPI) and transcription factor (TF) binding motifs for comprehensive analysis.
In recent years, spatial multi-omics technologies have rapidly emerged and revolutionized biomedical research. Among the technologies used in spatial transcriptomics and proteomics, the Digital Spatial Profiler (DSP) from nanoString is frequently relied upon to provide insights into intricate biological questions. Through our practical DSP experience over the past three years, we provide a comprehensive hands-on protocol and key handling guide, intended to aid the wider community in optimizing their work procedures.
To create a 3D scaffold and culture medium for patient-derived cancer samples, the 3D-autologous culture method (3D-ACM) incorporates a patient's own body fluid or serum. macrophage infection A patient's tumor cells and/or tissues are supported by 3D-ACM to thrive in a culture setting, which closely resembles their natural in-vivo condition. The aim is to preserve, to the greatest extent possible, the native biological properties of the tumor in a cultural environment. Two models employ this technique: (1) cells isolated from malignant ascites or pleural fluids, and (2) biopsy or surgically removed solid tumor tissues. The methodology behind the 3D-ACM models' procedures are elaborated upon in the subsequent sections.
By utilizing the mitochondrial-nuclear exchange mouse model, scientists can better understand the role of mitochondrial genetics in the development of disease. This report outlines the justification for their design, the methodologies used in their construction, and a succinct summary of how MNX mice have been utilized to explore the impact of mitochondrial DNA on multiple diseases, emphasizing cancer metastasis. Mitochondrial DNA variations, unique to different mouse lineages, exhibit both intrinsic and extrinsic impacts on metastatic efficiency by altering epigenetic patterns in the nuclear genome, impacting reactive oxygen species production, modulating the gut microbiota, and affecting the immune response against cancer cells. While this report primarily centers on cancer metastasis, MNX mice have demonstrably served as valuable tools for investigating the mitochondrial roles in other ailments as well.
The high-throughput technique, RNA sequencing (RNA-seq), is utilized for the quantification of mRNA within a biological sample. To identify genetic factors mediating drug resistance in cancers, differential gene expression between drug-resistant and sensitive forms is commonly investigated using this method. We present a complete experimental and bioinformatics methodology for isolating mRNA from human cell lines, constructing mRNA libraries suitable for next-generation sequencing, and subsequent bioinformatic analyses of the sequencing data.
The occurrence of DNA palindromes, a type of chromosomal alteration, is a frequent hallmark of tumorigenesis. Sequences of identical nucleotides to their reverse complements characterize these instances, frequently stemming from illegitimate DNA double-strand break repair, telomere fusion, or stalled replication forks. These represent common, adverse, early occurrences frequently associated with cancer. Employing low amounts of genomic DNA, this protocol describes the enrichment of palindromic sequences, accompanied by a bioinformatics pipeline that assesses enrichment and maps de novo palindromes formed in low-coverage whole-genome sequencing data.
Employing systems and integrative biological strategies, one can unravel the various levels of complexity found within cancer biology. By integrating lower-dimensional data and outcomes from lower-throughput wet laboratory studies with the large-scale, high-dimensional omics data-driven in silico discovery process, a more mechanistic understanding of the control, function, and execution of complex biological systems is achieved.