Optimal molecular profiling of tissue and tissue components: defining the best processing and microdissection methods for biomedical applications

Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue

Gene expression analysis of human kidney tissue is an important tool to understand homeostasis and disease pathophysiology. Increasing the resolution and depth of this technology and extending it to the level of cells within the tissue is needed. Although the use of single nuclear and single cell RNA sequencing has become widespread, the expression signatures of cells obtained from tissue dissociation do not maintain spatial context. Laser microdissection (LMD) based on specific fluorescent markers would allow the isolation of specific structures and cell groups of interest with known localization, thereby enabling the acquisition of spatially-anchored transcriptomic signatures in kidney tissue. We have optimized an LMD methodology, guided by a rapid fluorescence-based stain, to isolate five distinct compartments within the human kidney and conduct subsequent RNA sequencing from valuable human kidney tissue specimens. We also present quality control parameters to enable the assessment of adequacy of the collected specimens. The workflow outlined in this manuscript shows the feasibility of this approach to isolate sub-segmental transcriptomic signatures with high confidence. The methodological approach presented here may also be applied to other tissue types with substitution of relevant antibody markers.

Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools

Introduction

The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated.

Materials and Methods

To improve the efficiency of the cellular identification process, we describe a microdissection workflow that leverages recently developed and open source image analysis algorithms referred to as computer-aided laser dissection (CALD). CALD permits a computer algorithm to identify the cells of interest and drive the dissection process.

Results

We describe several "use cases" that demonstrate the integration of image analytic tools probabilistic pairwise Markov model, ImageJ, spatially invariant vector quantization (SIVQ), and eSeg onto the ThermoFisher Scientific ArcturusXT and Leica LMD7000 microdissection platforms.

Conclusions

The CALD methodology demonstrates the integration of image analysis tools with the microdissection workflow and shows the potential impact to clinical and life science applications.

Probing Glioblastoma Tissue Heterogeneity with Laser Capture Microdissection

Among various methods now available to isolate distinct cell populations or even single cells for DNA/RNA and proteomic analysis, laser capture microdissection (LCM) offers a unique opportunity to study cells in their topological contexts. This chapter focuses on the preparation of LCM membrane slides, tissue staining and laser microdissection of cells of interest from frozen or formalin-fixed, paraffin-embedded glioblastoma tissue.

Extensive rewiring of epithelial-stromal co-expression networks in breast cancer

BACKGROUND

Epithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis.

RESULTS

We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas.

CONCLUSIONS

Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.

SIVQ-LCM protocol for the ArcturusXT instrument

SIVQ-LCM is a new methodology that automates and streamlines the more traditional, user-dependent laser dissection process. It aims to create an advanced, rapidly customizable laser dissection platform technology. In this report, we describe the integration of the image analysis software Spatially Invariant Vector Quantization (SIVQ) onto the ArcturusXT instrument. The ArcturusXT system contains both an infrared (IR) and ultraviolet (UV) laser, allowing for specific cell or large area dissections. The principal goal is to improve the speed, accuracy, and reproducibility of the laser dissection to increase sample throughput. This novel approach facilitates microdissection of both animal and human tissues in research and clinical workflows.

Laser capture microdissection for the investigative pathologist

An important step in translational research is the validation of molecular findings from in vitro experiments using tissue specimens. However, tissue specimens are complex and contain a multitude of diverse cell populations that interfere with the molecular profiling data of a specific cell type. Laser capture microdissection (LCM) alleviates this issue by providing a valuable tool for the enrichment of a specific cell type within complex tissue samples. However, LCM and molecular analysis from tissue specimens can be complex and challenging due to numerous issues related with the tissue processing and its impact on the integrity of biomolecules in the specimen. The intricate nature of this application highlights the essential role a pathologist plays in translational research by contributing an expertise in histopathology, tissue handling, tissue analysis techniques, and clinical correlation of biological findings. The present review examines key practical aspects in tissue handling, specimen selection, quality control, and sample preparation for LCM and downstream molecular analyses that are a primary objective of the investigative pathologist.