Laser capture microdissection technology

Deciphering cellular complexity: advances and future directions in single-cell protein analysis

Single-cell protein analysis has emerged as a powerful tool for understanding cellular heterogeneity and deciphering the complex mechanisms governing cellular function and fate. This review provides a comprehensive examination of the latest methodologies, including sophisticated cell isolation techniques (Fluorescence-Activated Cell Sorting (FACS), Magnetic-Activated Cell Sorting (MACS), Laser Capture Microdissection (LCM), manual cell picking, and microfluidics) and advanced approaches for protein profiling and protein-protein interaction analysis. The unique strengths, limitations, and opportunities of each method are discussed, along with their contributions to unraveling gene regulatory networks, cellular states, and disease mechanisms. The importance of data analysis and computational methods in extracting meaningful biological insights from the complex data generated by these technologies is also highlighted. By discussing recent progress, technological innovations, and potential future directions, this review emphasizes the critical role of single-cell protein analysis in advancing life science research and its promising applications in precision medicine, biomarker discovery, and targeted therapeutics. Deciphering cellular complexity at the single-cell level holds immense potential for transforming our understanding of biological processes and ultimately improving human health.

Single-Cell Analysis of Bone-Marrow-Disseminated Tumour Cells

Metastasis frequently targets bones, where cancer cells from the primary tumour migrate to the bone marrow, initiating new tumour growth. Not only is bone the most common site for metastasis, but it also often marks the first site of metastatic recurrence. Despite causing over 90% of cancer-related deaths, effective treatments for bone metastasis are lacking, with current approaches mainly focusing on palliative care. Circulating tumour cells (CTCs) are pivotal in metastasis, originating from primary tumours and circulating in the bloodstream. They facilitate metastasis through molecular interactions with the bone marrow environment, involving direct cell-to-cell contacts and signalling molecules. CTCs infiltrate the bone marrow, transforming into disseminated tumour cells (DTCs). While some DTCs remain dormant, others become activated, leading to metastatic growth. The presence of DTCs in the bone marrow strongly correlates with future bone and visceral metastases. Research on CTCs in peripheral blood has shed light on their release mechanisms, yet investigations into bone marrow DTCs have been limited. Challenges include the invasiveness of bone marrow aspiration and the rarity of DTCs, complicating their isolation. However, advancements in single-cell analysis have facilitated insights into these elusive cells. This review will summarize recent advancements in understanding bone marrow DTCs using single-cell analysis techniques.

A modified dual preparatory method for improved isolation of nucleic acids from laser microdissected fresh-frozen human cancer tissue specimens

A central theme in cancer research is to increase our understanding of the cancer tissue microenvironment, which is comprised of a complex and spatially heterogeneous ecosystem of malignant and non-malignant cells, both of which actively contribute to an intervening extracellular matrix. Laser microdissection (LMD) enables histology selective harvest of cellular subpopulations from the tissue microenvironment for their independent molecular investigation, such as by high-throughput DNA and RNA sequencing. Although enabling, LMD often requires a labor-intensive investment to harvest enough cells to achieve the necessary DNA and/or RNA input requirements for conventional next-generation sequencing workflows. To increase efficiencies, we sought to use a commonplace dual preparatory (DP) procedure to isolate DNA and RNA from the same LMD harvested tissue samples. While the yield of DNA from the DP protocol was satisfactory, the RNA yield from the LMD harvested tissue samples was significantly poorer compared to a dedicated RNA preparation procedure. We determined that this low yield of RNA was due to incomplete partitioning of RNA in this widely used DP protocol. Here, we describe a modified DP protocol that more equally partitions nucleic acids and results in significantly improved RNA yields from LMD-harvested cells.

Proteomic profiling of laser capture microdissection kidneys from diabetic nephropathy patients

Diabetic nephropathy (DN) remains the primary cause of end-stage renal disease (ESRD), warranting equal attention and separate analysis of glomerular, tubular, and interstitial lesions in its diagnosis and intervention. This study aims to identify the specific proteomics characteristics of DN, and assess changes in the biological processes associated with DN. 5 patients with DN and 5 healthy kidney transplant donor control individuals were selected for analysis. The proteomic characteristics of glomeruli, renal tubules, and renal interstitial tissue obtained through laser capture microscopy (LCM) were studied using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Significantly, the expression of multiple heat shock proteins (HSPs), tubulins, and heterogeneous nuclear ribonucleoproteins (hnRNPs) in glomeruli and tubules was significantly reduced. Differentially expressed proteins (DEPs) in the glomerulus showed significant enrichment in pathways related to cell junctions and cell movement, including the regulation of actin cytoskeleton and tight junction. DEPs in renal tubules were significantly enriched in glucose metabolism-related pathways, such as glucose metabolism, glycolysis/gluconeogenesis, and the citric acid cycle. Moreover, the glycolysis/gluconeogenesis pathway was a co-enrichment pathway in both DN glomeruli and tubules. Notably, ACTB emerged as the most crucial protein in the protein-protein interaction (PPI) analysis of DEPs in both glomeruli and renal tubules. In this study, we delve into the unique proteomic characteristics of each sub-region of renal tissue. This enhances our understanding of the potential pathophysiological changes in DN, particularly the potential involvement of glycolysis metabolic disorder, glomerular cytoskeleton and cell junctions. These insights are crucial for further research into the identification of disease biomarkers and the pathogenesis of DN.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

Single-cell transcriptomics: background, technologies, applications, and challenges

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Digital PCR for Single-Cell Analysis

Single-cell analysis provides an overwhelming strategy for revealing cellular heterogeneity and new perspectives for understanding the biological function and disease mechanism. Moreover, it promotes the basic and clinical research in many fields at a single-cell resolution. A digital polymerase chain reaction (dPCR) is an absolute quantitative analysis technology with high sensitivity and precision for DNA/RNA or protein. With the development of microfluidic technology, digital PCR has been used to achieve absolute quantification of single-cell gene expression and single-cell proteins. For single-cell specific-gene or -protein detection, digital PCR has shown great advantages. So, this review will introduce the significance and process of single-cell analysis, including single-cell isolation, single-cell lysis, and single-cell detection methods, mainly focusing on the microfluidic single-cell digital PCR technology and its biological application at a single-cell level. The challenges and opportunities for the development of single-cell digital PCR are also discussed.

Microfluidic based single cell or droplet manipulation: Methods and applications

The isolation of single cell or droplet is first and crucial step to single-cell analysis, which is important for cancer research and diagnostic methods. This review provides an overview of technologies that are currently used or in development to realize the isolation. Microfluidic based manipulation is an emerging technology with the distinct advantages of miniaturization and low cost. Therefore, recent developments in microfluidic isolated methods have attracted extensive attention. We introduced herein five strategies based on microfluid: trap, microfluidic discrete manipulation, bioprinter, capillary and inertial force. For every technology, their basic principles and features were discussed firstly. Then some modified approaches and applications were listed as the extension. Finally, we compared the advantages and drawbacks of these methods, and analyzed the trend of the manipulation based on microfluidics.

Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution

Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.

Understanding plant pathogen interactions using spatial and single-cell technologies

Plants are in contact with diverse pathogens and microorganisms. Intense investigation over the last 30 years has resulted in the identification of multiple immune receptors in model and crop species as well as signaling overlap in surface-localized and intracellular immune receptors. However, scientists still have a limited understanding of how plants respond to diverse pathogens with spatial and cellular resolution. Recent advancements in single-cell, single-nucleus and spatial technologies can now be applied to plant-pathogen interactions. Here, we outline the current state of these technologies and highlight outstanding biological questions that can be addressed in the future.

Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives

Plants contain a large number of cell types and exhibit complex regulatory mechanisms. Studies at the single-cell level have gradually become more common in plant science. Single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics techniques have been combined to analyze plant development. These techniques have been used to study the transcriptomes and metabolomes of plant tissues at the single-cell level, enabling the systematic investigation of gene expression and metabolism in specific tissues and cell types during defined developmental stages. In this review, we present an overview of significant breakthroughs in spatial multi-omics in plants, and we discuss how these approaches may soon play essential roles in plant research.

Laser Capture Microdissection: A Gear for Pancreatic Cancer Research

The advancement in molecular techniques has been attributed to the quality and significance of cancer research. Pancreatic cancer (PC) is one of the rare cancers with aggressive behavior and a high mortality rate. The asymptomatic nature of the disease until its advanced stage has resulted in late diagnosis as well as poor prognosis. The heterogeneous character of PC has complicated cancer development and progression studies. The analysis of bulk tissues of the disease was insufficient to understand the disease, hence, the introduction of the single-cell separating technique aided researchers to decipher more about the specific cell population of tumors. This review gives an overview of the Laser Capture Microdissection (LCM) technique, one of the single-cell separation methods used in PC research.

Analysis of neuroendocrine clones in NSCLCs using an immuno-guided laser-capture microdissection-based approach

Clonal evolution and lineage plasticity are key contributors to tumor heterogeneity and response to treatment in cancer. However, capturing signal transduction events in coexisting clones remains challenging from a technical perspective. In this study, we developed and tested a signal-transduction-based workflow to isolate and profile coexisting clones within a complex cellular system like non-small cell lung cancers (NSCLCs). Cooccurring clones were isolated under immunohistochemical guidance using laser-capture microdissection, and cell signaling activation portraits were measured using the reverse-phase protein microarray. To increase the translational potential of this work and capture druggable vulnerabilities within different clones, we measured expression/activation of a panel of key drug targets and downstream substrates of FDA-approved or investigational agents. We isolated intermixed clones, including poorly represented ones (<5% of cells), within the tumor microecology and identified molecular characteristics uniquely attributable to cancer cells that undergo lineage plasticity and neuroendocrine transdifferentiation in NSCLCs.

Single-Cell Sequencing and Its Applications in Liver Cancer

As one of the most lethal cancers, primary liver cancer (PLC) has high tumor heterogeneity, including the heterogeneity between cancer cells. Traditional methods which have been used to identify tumor heterogeneity for a long time are based on large mixed cell samples, and the research results usually show average level of the cell population, ignoring the heterogeneity between cancer cells. In recent years, single-cell sequencing has been increasingly applied to the studies of PLCs. It can detect the heterogeneity between cancer cells, distinguish each cell subgroup in the tumor microenvironment (TME), and also reveal the clonal characteristics of cancer cells, contributing to understand the evolution of tumor. Here, we introduce the process of single-cell sequencing, review the applications of single-cell sequencing in the heterogeneity of cancer cells, TMEs, oncogenesis, and metastatic mechanisms of liver cancer, and discuss some of the current challenges in the field.

Differential proteomic analysis of laser-microdissected penetration glands of avian schistosome cercariae with a focus on proteins involved in host invasion

Schistosome invasive stages, cercariae, leave intermediate snail hosts, penetrate the skin of definitive hosts, and transform to schistosomula which migrate to the final location. During invasion, cercariae employ histolytic and other bioactive products of specialized holocrine secretory cells - postacetabular (PA) and circumacetabular (CA) penetration glands. Although several studies attempted to characterize protein composition of the in vitro-induced gland secretions in Schistosoma mansoni and Schistosoma japonicum, the results were somewhat inconsistent and dependent on the method of sample collection and processing. Products of both gland types mixed during their secretion did not allow localization of identified proteins to a particular gland. Here we compared proteomes of separately isolated cercarial gland cells of the avian schistosome Trichobilharzia szidati, employing laser-assisted microdissection and shotgun LC-MS/MS, thus obtaining the largest dataset so far of the representation and localization of cercarial penetration gland proteins. We optimized the methods of sample processing with cercarial bodies (heads) first. Alizarin-pre-stained, chemically non-fixed samples provided optimal results of MS analyses, and enabled us to distinguish PA and CA glands for microdissection. Using 7.5 x 10⁶ μm³ sample volume per gland replicate, we identified 3347 peptides assigned to 792 proteins, from which 461 occurred in at least two of three replicates in either gland type (PA = 455, 40 exclusive; CA = 421, six exclusive; 60 proteins differed significantly in their abundance between the glands). Peptidases of five catalytic types accounted for ca. 8% and 6% of reliably identified proteins in PA and CA glands, respectively. Invadolysin, nardilysin, cathepsins B2 and L3, and elastase 2b orthologs were the major gland endopeptidases. Two cystatins and a serpin were highly abundant peptidase inhibitors in the glands. While PA glands generally had rich enzymatic equipment, CA glands were conspicuously abundant in venom allergen-like proteins.

A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer

This article demonstrates a high-throughput technique to map cell images to cell positions. The technology uses a three-dimensional (3D) imaging flow cytometer to record multiparameter 3D cell images at a throughput of 1,000 cells/s and a cell placement robot to place the exiting cells from the imaging system on a filter plate in a first-in–first-out manner so the cells on the plate have the same order as the cells that are imaged. Innovative algorithms were developed to match the cell sequences from the imaging and placement modules to detect and eliminate errors to ensure high accuracy. The technology forms an unprecedented bridge between single-cell molecular analysis and single-cell image analysis to connect phenotype and genotype analysis with single-cell resolution.

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in–first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

Microfluidics for single cell analysis

Cells have several internal molecules that are present in low amounts and any fluctuation in its number drives a change in cell behavior. These molecules present inside the cells are continuously fluctuating, thus producing noises in the intrinsic environment and thereby directly affecting the cellular behavior. Single-cell analysis using microfluidics is an important tool for monitoring cell behavior by analyzing internal molecules. Several gene circuits have been designed for this purpose that are labeled with fluorescence encoding genes for monitoring cell dynamics and behavior. We discuss herewith designed and fabricated microfluidics devices that are used for trapping and tracking cells under controlled environmental conditions. This chapter highlights microfluidics chip for monitoring cells to promote their basic understanding.

Application of laser capture microdissection and PCR sequencing in the diagnosis of Coccidioides spp. infection: A case report and literature review in China

Coccidioidomycosis is endemic to California, Arizona, and Mexico. In recent years, the reported cases of coccidioidomycosis have increased in nonendemic regions. Here, we reported a case of imported pulmonary coccidioidomycosis in a Chinese patient. A 63-year-old man presented with dry cough and fatigue for 6 months, and a computed tomography scan revealed a solitary nodule in the right lower lung and small nodules in both lungs. The diagnosis of coccidioidomycosis was initially confirmed by histopathologic examination. The pathogen Coccidioides spp. was identified by laser capture microdissection (LCM) combined with subsequent molecular techniques based on the positive histopathologic features. Additionally, we reviewed 47 reported cases of coccidioidomycosis in China. The number of reported cases is increasing, and the incidence of disseminated infection has exhibited a trend of shifting towards healthy young adults in China. Since clinical presentations and imaging findings lack specificity, a majority of domestic cases of coccidioidomycosis were initially misdiagnosed as tumours or tuberculosis. Moreover, the diagnosis of endemic mycoses may be challenging because of their rarity and the limited availability of diagnostic tests. The diagnosis was mainly confirmed by histopathological examination. The species involved were identified based on positive cultures in only 4 cases. To our knowledge, this is the first study to use LCM and molecular techniques to identify Coccidioides spp. in the histopathologically positive but uncultivable specimen. Comparing with previous reported studies, LCM combined with nucleic acid amplification techniques improve the ability of species identification for the timely diagnosis of coccidioidomycosis.

Microfluidics applications for high-throughput single cell sequencing

The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.

Multiomic Analyses of Dopaminergic Neurons Isolated from Human Substantia Nigra in Parkinson's Disease: A Descriptive and Exploratory Study

Dopaminergic neurons (DA) of the substantia nigra pars compacta (SNpc) selectively and progressively degenerate in Parkinson’s disease (PD). Until now, molecular analyses of DA in PD have been limited to genomic or transcriptomic approaches, whereas, to the best of our knowledge, no proteomic or combined multiomic study examining the protein profile of these neurons is currently available. In this exploratory study, we used laser capture microdissection to extract regions from DA in 10 human SNpc obtained at autopsy in PD patients and control subjects. Extracted RNA and proteins were identified by RNA sequencing and nanoliquid chromatography–mass spectrometry, respectively, and the differential expression between PD and control group was assessed. Qualitative analyses confirmed that the microdissection protocol preserves the integrity of our samples and offers access to specific molecular pathways. This multiomic analysis highlighted differential expression of 52 genes and 33 proteins, including molecules of interest already known to be dysregulated in PD, such as LRP2, PNMT, CXCR4, MAOA and CBLN1 genes, or the Aldehyde dehydrogenase 1 protein. On the other hand, despite the same samples were used for both analyses, correlation between RNA and protein expression was low, as exemplified by the CST3 gene encoding for the cystatin C protein. This is the first exploratory study analyzing both gene and protein expression of laser-dissected neuronal parts from SNpc in PD. Data are available via ProteomeXchange with identifier PXD024748 and via GEO with identifier GSE 169755.

The Combination of Single-Cell and Next-Generation Sequencing Can Reveal Mosaicism for BRCA2 Mutations and the Fine Molecular Details of Tumorigenesis

Germline mutations in the BRCA1 and BRCA2 genes are responsible for hereditary breast and ovarian cancer syndrome. Germline and somatic BRCA1/2 mutations may define therapeutic targets and refine cancer treatment options. However, routine BRCA diagnostic approaches cannot reveal the exact time and origin of BRCA1/2 mutation formation, and thus, the fine details of their contribution to tumor progression remain less clear. Here, we establish a diagnostic pipeline using high-resolution microscopy and laser microcapture microscopy to test for BRCA1/2 mutations in the tumor at the single-cell level, followed by deep next-generation sequencing of various tissues from the patient. To demonstrate the power of our approach, here, we describe a detailed single-cell-level analysis of an ovarian cancer patient we found to exhibit constitutional somatic mosaicism of a pathogenic BRCA2 mutation. Employing next-generation sequencing, BRCA2 c.7795G>T, p.(Glu2599Ter) was detected in 78% of reads in DNA extracted from ovarian cancer tissue and 25% of reads in DNA derived from peripheral blood, which differs significantly from the expected 50% of a hereditary mutation. The BRCA2 mutation was subsequently observed at 17-20% levels in the normal ovarian and buccal tissue of the patient. Together, our findings suggest that this mutation occurred early in embryonic development. Characterization of the mosaic mutation at the single-cell level contributes to a better understanding of BRCA mutation formation and supports the concept that the combination of single-cell and next-generation sequencing methods is advantageous over traditional mutational analysis methods. This study is the first to characterize constitutional mosaicism down to the single-cell level, and it demonstrates that BRCA2 mosaicism occurring early during embryogenesis can drive tumorigenesis in ovarian cancer.

Molecular Characterization of Testicular Germ Cell Tumors Using Tissue Microdissection

Testicular germ cell tumors are among the most common malignancies seen in children and young adults. Genomic studies have identified characteristic molecular profiles in testicular cancer, which are associated with histologic subtypes and may predict clinical behavior including treatment responses. Emerging molecular technologies analyzing tumor genomics, transcriptomics, and proteomics may now guide precision management of testicular tumors. Laser-assisted microdissection methods such as laser capture microdissection efficiently isolate selected tumor cells from routine pathology specimens, avoiding contamination from nontarget cell populations. Laser capture microdissection in combination with next generation sequencing makes precise high throughput genetic evaluation effective and efficient. The use of laser capture microdissection (LCM) for molecular testing may translate into great benefits for the clinical management of patients with testicular cancers. This review discusses application protocols for laser-assisted microdissection to investigate testicular germ cell tumors.

Cell-blocks and other ancillary studies (including molecular genetic tests and proteomics)

Many types of elective ancillary tests may be required to support the cytopathologic interpretations. Most of these tests can be performed on cell-blocks of different cytology specimens. The cell-block sections can be used for almost any special stains including various histochemistry stains and for special stains for different microorganisms including fungi, Pneumocystis jirovecii (carinii), and various organisms including acid-fast organisms similar to the surgical biopsy specimens. Similarly, in addition to immunochemistry, different molecular tests can be performed on cell-blocks. Molecular tests broadly can be divided into two main types Molecular genetic tests and Proteomics.

Comparative transcriptomic analysis identifies distinct molecular signatures and regulatory networks of chondroclasts and osteoclasts

BACKGROUND

Chondroclasts and osteoclasts have been previously identified as the cells capable of resorbing mineralized cartilage and bone matrices, respectively. While both cell types appear morphologically similar, contain comparable ultrastructural features, and express tartrate-resistant acid phosphatase (TRAP), however, no information is available about the genomic similarities and differences between osteoclasts and chondroclasts.

METHODS

To address this question, we laser captured homogeneous populations of TRAP-positive cells that interact with bone (osteoclasts) and TRAP-positive cells that interact with mineralized cartilage (chondroclasts) on the same plane from murine femoral fracture callus sections. We then performed a global transcriptome profiling of chondroclasts and osteoclasts by utilizing a mouse genome Agilent GE 4X44K V2 microarray platform. Multiple computational approaches and interaction networks were used to analyze the transcriptomic landscape of osteoclasts and chondroclasts.

RESULTS

Our systematic and comprehensive analyses using hierarchical clustering and principal component analysis (PCA) demonstrate that chondroclasts and osteoclasts are transcriptionally distinct cell populations and exhibit discrete transcriptomic signatures as revealed by multivariate analysis involving scatter plot, volcano plot, and heatmap analysis. TaqMan qPCR was used to validate the microarray results. Intriguingly, the functional enrichment and integrated network analyses revealed distinct Gene Ontology terms and molecular pathways specific to chondroclasts and osteoclasts and further suggest that subsets of metabolic genes were specific to chondroclasts. Protein-protein interaction (PPI) network analysis showed an abundance of structured networks of metabolic pathways, ATP synthesis, and proteasome pathways in chondroclasts. The regulatory network analysis using transcription factor-target gene network predicted a pool of genes including ETV6, SIRT1, and ATF1 as chondroclast-specific gene signature.

CONCLUSIONS

Our study provides an important genetic resource for further exploration of chondroclast function in vivo. To our knowledge, this is the first demonstration of genetic landscape of osteoclasts from chondroclasts identifying unique molecular signatures, functional clustering, and interaction network.

Methods for Single-Cell Isolation and Preparation

Within the last decade, single-cell analysis has revolutionized our understanding of cellular processes and heterogeneity across all disciplines of life science. As the transcriptome, genome, or epigenome of individual cells can nowadays be analyzed at low cost and in high-throughput within a few days by modern techniques, tremendous improvements in disease diagnosis on the one hand and the investigation of disease-relevant mechanisms on the other were achieved so far. This relies on the parallel development of reliable cell capturing and single-cell sequencing approaches that have paved the way for comprehensive single-cell studies. Apart from single-cell isolation methods in high-throughput, a variety of methods with distinct specializations were developed, allowing for correlation of transcriptomics with cellular parameters like electrophysiology or morphology.For all single-cell-based approaches, accurate and reliable isolation with proper quality controls is prerequisite, whereby different options exist dependent on sample type and tissue properties. Careful consideration of an appropriate method is required to avoid incorrect or biased data that may lead to misinterpretations.In this chapter, we will provide a broad overview of the current state of the art in matters of single-cell isolation methods mostly applied for sequencing-based downstream analysis, and their respective advantages and drawbacks. Distinct technologies will be discussed in detail addressing key parameters like sample compatibility, viability, purity, throughput, and isolation efficiency.

De novo compartment deconvolution and weight estimation of tumor samples using DECODER

Tumors are mixtures of different compartments. While global gene expression analysis profiles the average expression of all compartments in a sample, identifying the specific contribution of each compartment remains a challenge. With the increasing recognition of the importance of non-neoplastic components, the ability to breakdown the gene expression contribution of each is critical. Here, we develop DECODER, an integrated framework which performs de novo deconvolution and single-sample compartment weight estimation. We use DECODER to deconvolve 33 TCGA tumor RNA-seq data sets and show that it may be applied to other data types including ATAC-seq. We demonstrate that it can be utilized to reproducibly estimate cellular compartment weights in pancreatic cancer that are clinically meaningful. Application of DECODER across cancer types advances the capability of identifying cellular compartments in an unknown sample and may have implications for identifying the tumor of origin for cancers of unknown primary.

Separating different cell compartments from bulk gene expression data can be challenging. Here the authors present DECODER, which can perform de novo deconvolutions on non-negative matrices including microarray, RNA-seq and ATAC-seq data sets.

Differential detachment of intact and viable cells of different sizes using laser-induced microbubbles

Single cell isolation is a prerequisite for the analysis of rare or small cell subtypes. Here, we selectively detach single cells in a heterogeneous population comprised of different morphological subtypes whose sizes vary in body and extension. Such a cellular environment is first accommodated for by a photomechanical method in which pulsed laser irradiation produces microbubbles from a polymer substrate, thus pushing out and detaching cultured cells in an intact, viable, and spatially tailored way. While this has previously only bene used at a very low cell density with lack of quantitative characterization, we determine optimal detachment conditions for different cell sizes in terms of an optical fluence and the number of laser pulses. Importantly, our approach is employed to isolate cancer cells with inherent size variation and elucidate cellular heterogeneity in drug sensitivity: i.e., higher resistance for larger cell size. For cells detached by laser-induced microbubbles, morphology, proliferation, and viability are compared with those of conventional trypsin-treated cells detached without any spatial selectivity. These results support the suitability of our photomechanical method for biochemical screen and secondary analysis of cells with unusual responses.

High throughput single cell separation and identification using a self-priming isometric and Equant screw valve-based (SIES) microfluidic chip

The emergence of various single cell separation and identification platforms has greatly promoted the development of single cell research. Among these platforms, microfluidic chip-based strategies occupy a significant position in single cell separation and identification. Here, we proposed a self-priming isometric and Equant screw valve-based microfluidic chip (SIES chip) for high throughput single cell isolation and identification. With several special designs, such as a peripheral water tank to balance negative pressure distribution in a marginal area of the chip, a screw valve to preserve the suction power during the step-by-step sample loading, and multistage branching "T" shape channels to separate cells evenly into the chambers, up to 2000 single cells can be well dispersed and analyzed at the same time using this chip. We applied this chip for the isolation and identification of single A549 cells targeting the activated leukocyte cell adhesion molecule (ALCAM) gene. The results showed that only a small proportion (approximately 5.1%) of A549 cells expressed ALCAM, which can potentially provide a reference for A549 cell reclassification. Besides being inexpensive, user-friendly and portable, our chip can be used in some resource-limited settings and may have a great potential in POC (Point-of-Care) applications.

Whole genome MBD-seq and RRBS analyses reveal that hypermethylation of gastrointestinal hormone receptors is associated with gastric carcinogenesis

DNA methylation is a regulatory mechanism in epigenetics that is frequently altered during human carcinogenesis. To detect critical methylation events associated with gastric cancer (GC), we compared three DNA methylomes from gastric mucosa (GM), intestinal metaplasia (IM), and gastric tumor (GT) cells that were microscopically dissected from an intestinal-type early gastric cancer (EGC) using methylated DNA binding domain sequencing (MBD-seq) and reduced representation bisulfite sequencing (RRBS) analysis. In this study, we focused on differentially methylated promoters (DMPs) that could be directly associated with gene expression. We detected 2,761 and 677 DMPs between the GT and GM by MBD-seq and RRBS, respectively, and for a total of 3,035 DMPs. Then, 514 (17%) of all DMPs were detected in the IM genome, which is a precancer of GC, supporting that some DMPs might represent an early event in gastric carcinogenesis. A pathway analysis of all DMPs demonstrated that 59 G protein-coupled receptor (GPCR) genes linked to the hypermethylated DMPs were significantly enriched in a neuroactive ligand–receptor interaction pathway. Furthermore, among the 59 GPCRs, six GI hormone receptor genes (NPY1R, PPYR1, PTGDR, PTGER2, PTGER3, and SSTR2) that play an inhibitory role in the secretion of gastrin or gastric acid were selected and validated as potential biomarkers for the diagnosis or prognosis of GC patients in two cohorts. These data suggest that the loss of function of gastrointestinal (GI) hormone receptors by promoter methylation may lead to gastric carcinogenesis because gastrin and gastric acid have been known to play a role in cell differentiation and carcinogenesis in the GI tract.

A sequencing study reveals abnormal changes to DNA that set the stage for stomach cancer development. DNA methylation, the addition of methyl groups to alter DNA activity, is often disrupted in human cancers. Yong Sung Kim at the Korea Research Institute of Bioscience and Biotechnology (KRIBB) in Daejeon, South Korea, and co-workers used sequencing technogy to identify critical methylation changes in stomach epithelial cells, intestinal metaplasia lesion and tumor cells during early-stage gastric cancer. The team found 3,035 abnormally methylated DNA regions related to the expression of particular genes. Further analysis identified six hormone receptor genes directly involved with stomach acid secretion, whose altered expression was linked to over-methylated DNA regions. Loss of function within these six genes may lead to gastric cancer, and their expression levels could be valuable biomarkers for the disease.

Beyond the H&E: Advanced Technologies for in situ Tissue Biomarker Imaging

For decades, histopathology with routine hematoxylin and eosin staining has been and remains the gold standard for reaching a morphologic diagnosis in tissue samples from humans and veterinary species. However, within the past decade, there has been exponential growth in advanced techniques for in situ tissue biomarker imaging that bridge the divide between anatomic and molecular pathology. It is now possible to simultaneously observe localization and expression magnitude of multiple protein, nucleic acid, and molecular targets in tissue sections and apply machine learning to synthesize vast, image-derived datasets. As these technologies become more sophisticated and widely available, a team-science approach involving subspecialists with medical, engineering, and physics backgrounds is critical to upholding quality and validity in studies generating these data. The purpose of this manuscript is to detail the scientific premise, tools and training, quality control, and data collection and analysis considerations needed for the most prominent advanced imaging technologies currently applied in tissue sections: immunofluorescence, in situ hybridization, laser capture microdissection, matrix-assisted laser desorption ionization imaging mass spectrometry, and spectroscopic/optical methods. We conclude with a brief overview of future directions for ex vivo and in vivo imaging techniques.

TargetClone: A multi-sample approach for reconstructing subclonal evolution of tumors

Most tumors are composed of a heterogeneous population of subclones. A more detailed insight into the subclonal evolution of these tumors can be helpful to study progression and treatment response. Problematically, tumor samples are typically very heterogeneous, making deconvolving individual tumor subclones a major challenge. To overcome this limitation, reducing heterogeneity, such as by means of microdissections, coupled with targeted sequencing, is a viable approach. However, computational methods that enable reconstruction of the evolutionary relationships require unbiased read depth measurements, which are commonly challenging to obtain in this setting. We introduce TargetClone, a novel method to reconstruct the subclonal evolution tree of tumors from single-nucleotide polymorphism allele frequency and somatic single-nucleotide variant measurements. Furthermore, our method infers copy numbers, alleles and the fraction of the tumor component in each sample. TargetClone was specifically designed for targeted sequencing data obtained from microdissected samples. We demonstrate that our method obtains low error rates on simulated data. Additionally, we show that our method is able to reconstruct expected trees in a testicular germ cell cancer and ovarian cancer dataset. The TargetClone package including tree visualization is written in Python and is publicly available at https://github.com/UMCUGenetics/targetclone.

PAXgene-fixed paraffin-embedded sample is applicable to laser capture microdissection with well-balanced RNA quality and tissue morphology

Assessing how gene expression analysis by RNA sequencing (RNA-Seq) correlates to a unique morphology is increasingly necessary, and laser capture microdissection (LCM) is a critical research tool for discovering the genes responsible in a region of interest (ROI). Because RNA-Seq requires high-quality RNA, a sample preparation procedure that can preserve morphology and give the required quality of RNA is essential. A PAXgene®-fixed paraffin-embedded (XFPE) block can satisfy the need for high-quality RNA, but there are few reports on adapting the method for LCM, such as how small an ROI is analyzable by RNA-Seq. In this study, we confirmed the morphology and preservation of RNA in XFPE and then assessed the relationship between the size of pieces cut by LCM and their RNA quality. In XFPE, the morphology was similar to that in alcohol-based fixed samples, the quality of the RNA extracted from a whole sample was excellent, that is equivalent to that of a fresh frozen sample, and the quality was maintained over one year later. Three sizes of pieces—large (25,000 µm²), medium (5,000 µm²), and small (1,000 µm²)—were cut by LCM so that the total areas of the sections cut per size were the same. RNA quality was found to be best preserved when tissue was cut into pieces of over 5,000 µm². In summary, XFPE exhibits good morphology and excellent preservation of RNA quality. Furthermore, it can be a good tool when used with LCM and RNA-Seq, giving well-balanced RNA quality and tissue morphology in the ROI.

Shotgun Lipidomics Combined with Laser Capture Microdissection: A Tool To Analyze Histological Zones in Cryosections of Tissues

Shotgun analysis provides a quantitative snapshot of the lipidome composition of cells, tissues, or model organisms; however, it does not elucidate the spatial distribution of lipids. Here we demonstrate that shotgun analysis could quantify low-picomole amounts of lipids isolated by laser capture microdissection (LCM) of hundred micrometer-sized histological zones visualized at the cryosections of tissues. We identified metabolically distinct periportal (pp) and pericentral (pc) zones by immunostaining of 20 μm thick cryosections of a healthy mouse liver. LCM was used to ablate, catapult, and collect the tissue material from 10 to 20 individual zones covering a total area of 0.3-0.5 mm² and containing ca. 500 cells. Top-down shotgun profiling relying upon computational stitching of 61 targeted selective ion monitoring ( t-SIM) spectra quantified more than 200 lipid species from 17 lipid classes including glycero- and glycerophospholipids, sphingolipids, cholesterol esters, and cholesterol. Shotgun LCM revealed the overall commonality of the full lipidome composition of pp and pc zones along with significant ( p < 0.001) difference in the relative abundance of 13 lipid species. Follow-up proteomics analyses of pellets recovered from an aqueous phase saved after the lipid extraction identified 13 known and 7 new protein markers exclusively present in pp or in pc zones and independently validated the specificity of their visualization, isolation, and histological assignment.

An Insight into the Difficulties in the Discovery of Specific Biomarkers of Limbal Stem Cells

Keeping the integrity and transparency of the cornea is the most important issue to ensure normal vision. There are more than 10 million patients going blind due to the cornea diseases worldwide. One of the effective ways to cure corneal diseases is corneal transplantation. Currently, donations are the main source of corneas for transplantation, but immune rejection and a shortage of donor corneas are still serious problems. Graft rejection could cause transplanted cornea opacity to fail. Therefore, bioengineer-based corneas become a new source for corneal transplantation. Limbal stem cells (LSCs) are located at the basal layer in the epithelial palisades of Vogt, which serve a homeostatic function for the cornea epithelium and repair the damaged cornea. LSC-based transplantation is one of the hot topics currently. Clinical data showed that the ratio of LSCs to total candidate cells for a transplantation has a significant impact on the effectiveness of the transplantation. It indicates that it is very important to accurately identify the LSCs. To date, several putative biomarkers of LSCs have been widely reported, whereas their specificity is controversial. As reported, the identification of LSCs is based on the characteristics of stem cells, such as a nuclear-to-cytoplasm ratio (N/C) ≥ 0.7, label-retaining, and side population (SP) phenotype. Here, we review recently published data to provide an insight into the circumstances in the study of LSC biomarkers. The particularities of limbus anatomy and histochemistry, the limits of the current technology level for LSC isolation, the heterogeneity of LSCs and the influence of enzyme digestion are discussed. Practical approaches are proposed in order to overcome the difficulties in basic and applied research for LSC-specific biomarkers.

Site-specific gene expression analysis from archived human intestine samples combining laser-capture microdissection and multiplexed color-coded probes

BACKGROUND

Alterations of site-specific gene expression profiles in disease-relevant networks within the different layers of the intestinal wall may contribute to the onset and clinical course of gastrointestinal disorders. To date, no systematic analysis has assessed and compared sub-regional gene expression patterns in all distinct layers of the gut using fresh frozen human samples. Our aim was to establish an optimized protocol for site-specific RNA isolation in order to achieve maximum RNA quality and amount for subsequent gene expression analysis combining laser-capture microdissection (LCM) with a probe-based technology, the NanoString nCounter Analysis system.

METHODS

Four full-thickness colon samples from patients who underwent surgery due to pathological conditions were processed and separated into epithelium, lamina propria, myenteric plexus, submucosa, and tunica muscularis by LCM. Site-specific marker expression by nCounter technology was performed on total RNA from each sub-region, respectively.

KEY RESULTS

Collecting ~10 mm² (~100 000-250 000 cells) of tissue from the epithelial layer, lamina propria, and myenteric plexus provided sufficient amounts of RNA of appropriate quality for subsequent analyses. In contrast, ~40 mm² (~250 000-650 000 cells) of tissue were dissected from the less cell-rich submucosal and tunica muscularis layer. nCounter analysis revealed a site-specific expression pattern of marker genes in the different layers of the colonic wall which were highly correlating (r > .9).

CONCLUSIONS AND INFERENCES

LCM in combination with nCounter expression analysis enables site-specific, sensitive, reliable detection, and quantification of mRNA from histologically heterogeneous tissues.

Stromal TRIM28-associated signaling pathway modulation within the colorectal cancer microenvironment

BACKGROUND

Stromal gene expression patterns predict patient outcomes in colorectal cancer. TRIM28 is a transcriptional co-repressor that regulates an abundance of genes through the KRAB domain family of transcription factors. We have previously shown that stromal expression of TRIM28 is a marker of disease relapse and poor survival in colorectal cancer. Here, we perform differential epithelium-stroma proteomic network analyses to characterize signaling pathways associated with TRIM28 within the tumor microenvironment.

METHODS

Reverse phase protein arrays were generated from laser capture micro-dissected carcinoma and stromal cells from fresh frozen colorectal cancer tissues. Phosphorylation and total protein levels were measured for 30 cancer-related signaling pathway endpoints. Strength and direction of associations between signaling endpoints were identified using Spearman's rank-order correlation analysis and compared to TRIM28 levels. Expression status of TRIM28 in tumor epithelium and stromal fibroblasts was assessed using IHC in formalin fixed tissue and the epithelium to stroma protein expression ratio method.

RESULTS

We found distinct proteomic networks in the epithelial and stromal compartments which were linked to expression levels of TRIM28. Low levels of TRIM28 in tumor stroma (high epithelium: stroma ratio) were found in 10 out of 19 cases. Upon proteomic network analyses, these stromal high ratio cases revealed moderate signaling pathway similarity exemplified by 76 significant Spearman correlations (ρ ≥ 0.75, p ≤ 0.01). Furthermore, low levels of stromal TRIM28 correlated with elevated MDM2 levels in tumor epithelium (p = 0.01) and COX-2 levels in tumor stroma (p = 0.002). Low TRIM28 epithelium to stroma ratios were associated with elevated levels of caspases 3 and 7 in stroma (p = 0.041 and p = 0.036) and an increased signaling pathway similarity in stromal cells with 81 significant Spearman correlations (ρ ≥ 0.75, p ≤ 0.01).

CONCLUSIONS

By dissecting TRIM28-associated pathways in stromal fibroblasts and epithelial tumor cells, we performed comprehensive proteomic analyses of molecular networks within the tumor microenvironment. We found modulation of several signaling pathways associated with TRIM28, which may be attributed to the pleiotropic properties of TRIM28 through its translational suppression of the family of KRAB domain transcription factors in tumor stromal compartments.

Testicular germ line cell identification, isolation, and transplantation in two North American catfish species

Our aim was to transplant blue catfish germ line stem cells into blastulae of triploid channel catfish embryos to produce interspecific xenogenic catfish. The morphological structure of the gonads of blue catfish (Ictalurus furcatus) in ~ 90- to 100-day-old juveniles, two-year-old juveniles, and mature adults was studied histologically. Both oogonia (12-15 μm, diameter with distinct nucleus 7-8 μm diameter) and spermatogonia (12-15 μm, with distinct nucleus 6-7.5 μm diameter) were found in all ages of fish. The percentage of germ line stem cells was higher in younger blue catfish of both sexes. After the testicular tissue was trypsinized, a discontinuous density gradient centrifugation was performed using 70, 45, and 35% Percoll to enrich the percentage of spermatogonial stem cells (SSCs). Four distinct cell bands were generated after the centrifugation. It was estimated that 50% of the total cells in the top band were type A spermatogonia (diameter 12-15 μm) and type B spermatogonia (diameter 10-11 μm). Germ cells were confirmed with expression of vasa. Blastula-stage embryos of channel catfish (I. punctatus) were injected with freshly dissociated blue catfish testicular germ cells as donor cells for transplantation. Seventeen days after the transplantation, 33.3% of the triploid channel catfish fry were determined to be xenogenic catfish. This transplantation technique was efficient, and these xenogenic channel catfish need to be grown to maturity to verify their reproductive capacity and to verify that for the first time SSCs injected into blastulae were able to migrate to the genital ridge and colonize. These results open the possibility of artificially producing xenogenic channel catfish males that can produce blue catfish sperm and mate with normal channel catfish females naturally. The progeny would be all C × B hybrid catfish, and the efficiency of hybrid catfish production could be improved tremendously in the catfish industry.

Platforms for Single-Cell Collection and Analysis

Single-cell analysis has become an established method to study cell heterogeneity and for rare cell characterization. Despite the high cost and technical constraints, applications are increasing every year in all fields of biology. Following the trend, there is a tremendous development of tools for single-cell analysis, especially in the RNA sequencing field. Every improvement increases sensitivity and throughput. Collecting a large amount of data also stimulates the development of new approaches for bioinformatic analysis and interpretation. However, the essential requirement for any analysis is the collection of single cells of high quality. The single-cell isolation must be fast, effective, and gentle to maintain the native expression profiles. Classical methods for single-cell isolation are micromanipulation, microdissection, and fluorescence-activated cell sorting (FACS). In the last decade several new and highly efficient approaches have been developed, which not just supplement but may fully replace the traditional ones. These new techniques are based on microfluidic chips, droplets, micro-well plates, and automatic collection of cells using capillaries, magnets, an electric field, or a punching probe. In this review we summarize the current methods and developments in this field. We discuss the advantages of the different commercially available platforms and their applicability, and also provide remarks on future developments.

Evaluation of digital real-time PCR assay as a molecular diagnostic tool for single-cell analysis

In a single-cell study, isolating and identifying single cells are essential, but these processes often require a large investment of time or money. The aim of this study was to isolate and analyse single cells using a novel platform, the PanelChip™ Analysis System, which includes 2500 microwells chip and a digital real-time polymerase chain reaction (dqPCR) assay, in comparison with a standard PCR (qPCR) assay. Through the serial dilution of a known concentration standard, namely pUC19, the accuracy and sensitivity levels of two methodologies were compared. The two systems were tested on the basis of expression levels of the genetic markers vimentin, E-cadherin, N-cadherin and GAPDH in A549 lung carcinoma cells at two known concentrations. Furthermore, the influence of a known PCR inhibitor commonly found in blood samples, heparin, was evaluated in both methodologies. Finally, mathematical models were proposed and separation method of single cells was verified; moreover, gene expression levels during epithelial-mesenchymal transition in single cells under TGFβ1 treatment were measured. The drawn conclusion is that dqPCR performed using PanelChip™ is superior to the standard qPCR in terms of sensitivity, precision, and heparin tolerance. The dqPCR assay is a potential tool for clinical diagnosis and single-cell applications.

Molecular similarities and differences from human pulmonary fibrosis and corresponding mouse model: MALDI imaging mass spectrometry in comparative medicine

Animal models can reproduce some model-specific aspects of human diseases, but some animal models translate poorly or fail to translate to the corresponding human disease. Here, we develop a strategy to systematically compare human and mouse tissues, and conduct a proof-of-concept experiment to identify molecular similarities and differences using patients with idiopathic pulmonary fibrosis and a bleomycin-induced fibrosis mouse model. Our novel approach employs high-throughput tissue microarrays (TMAs) of humans and mice, high-resolution matrix-assisted laser desorption/ionization-Fourier transform-ion cyclotron resonance-mass spectrometry imaging (MALDI-FT-ICR-MSI) to spatially resolve mass spectra at the level of specific metabolites, and hierarchical clustering and pathway enrichment analysis to identify functionally similar/different molecular patterns and pathways in pathological lesions of humans and mice. We identified a large number of common molecules (n=1366) and fewer exclusive molecules in humans (n=83) and mice (n=54). Among the common molecules, the 'ascorbate and aldarate metabolism' pathway had the highest similarity in human and mouse lesions. This proof-of-concept study demonstrates that our novel strategy employing a reliable and easy-to-perform experimental design accurately identifies pathways and factors that can be directly compared between animal models and human diseases.

Laser Capture Microdissection: Insights into Methods and Applications

Laser capture microdissection is a non-molecular, minimally disruptive method to obtain cytologically and/or phenotypically defined cells or groups of cells from heterogeneous tissues. Its advantages include efficient rapid and precise procurement of cells. The potential disadvantages include time consuming, expensive, and limited by the need for a pathologist for recognition of distinct subpopulations within a specified sample. Overall it is versatile allowing the preparation of homogenous isolates of specific subpopulations of cells from which DNA/RNA or protein can be extracted for RT-PCR, quantitative PCR, next-generation sequencing, immunoblot blot analyses, and mass spectrometry.

Laser microdissection: A powerful tool for genomics at cell level

Laser microdissection (LM) has become widely democratized over the last fifteen years. Instruments have evolved to offer more powerful and efficient lasers as well as new options for sample collection and preparation. Technological evolutions have also focused on the post-microdissection analysis capabilities, opening up investigations in all disciplines of experimental and clinical biology, thanks to the advent of new high-throughput methods of genome analysis, including RNAseq and proteomics, now globally known as microgenomics, i.e. analysis of biomolecules at the cell level. In spite of the advances these rapidly developing methods have allowed, the workflow for sampling and collection by LM remains a critical step in insuring sample integrity in terms of histology (accurate cell identification) and biochemistry (reliable analyzes of biomolecules). In this review, we describe the sample processing as well as the strengths and limiting factors of LM applied to the specific selection of one or more cells of interest from a heterogeneous tissue. We will see how the latest developments in protocols and methods have made LM a powerful and sometimes essential tool for genomic and proteomic analyzes of tiny amounts of biomolecules extracted from few cells isolated from a complex tissue, in their physiological context, thus offering new opportunities for understanding fundamental physiological and/or patho-physiological processes.

A sequential Monte Carlo approach to gene expression deconvolution

High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of such data. In order to make conclusions on gene expresssion data obtained from heterogeneous samples, methods such as microdissection and flow cytometry have been employed to physically separate the constituting cell types. However, these manual approaches are time consuming when measuring the responses of multiple cell types simultaneously. In addition, exposed samples, on many occasions, end up being contaminated with external perturbations and this may result in an altered yield of molecular content. In this paper, we model the heterogeneous gene expression data using a Bayesian framework, treating the cell type proportions and the cell-type specific expressions as the parameters of the model. Specifically, we present a novel sequential Monte Carlo (SMC) sampler for estimating the model parameters by approximating their posterior distributions with a set of weighted samples. The SMC framework is a robust and efficient approach where we construct a sequence of artificial target (posterior) distributions on spaces of increasing dimensions which admit the distributions of interest as marginals. The proposed algorithm is evaluated on simulated datasets and publicly available real datasets, including Affymetrix oligonucleotide arrays and national center for biotechnology information (NCBI) gene expression omnibus (GEO), with varying number of cell types. The results obtained on all datasets show a superior performance with an improved accuracy in the estimation of cell type proportions and the cell-type specific expressions, and in addition, more accurate identification of differentially expressed genes when compared to other widely known methods for blind decomposition of heterogeneous gene expression data such as Dsection and the nonnegative matrix factorization (NMF) algorithms. MATLAB implementation of the proposed SMC algorithm is available to download at https://github.com/moyanre/smcgenedeconv.git.

Laser-Capture Microdissection of Maize Kernel Compartments for RNA-Seq-Based Expression Analysis

Laser-capture microdissection (LCM) enables isolation of single cells or groups of cells for a variety of downstream applications including transcriptome profiling. Recently, this methodology has found a more widespread use particularly with the advent of next-generation sequencing techniques that enable deep profiling of the limited amounts of RNA obtained from fixed or frozen sections. When used with fixed tissues, a major experimental challenge is to balance the tissue integrity needed for microscopic visualization of the cell types of interest with that of the RNA quality necessary for deep profiling. Complex biological structures such as seeds or kernels pose an especially difficult case in this context as in many instances the key internal structures such as the embryo and the endosperm are relatively inaccessible. Here, we present an optimized LCM protocol for maize kernel that has been developed specifically to enable profiling of the early stages of endosperm development using RNA-Seq.

Single-cell genomic analysis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) incidence or rates have increased dramatically recently with little improvement in patient outcomes. There is an unmet need in HNSCC to develop reliable molecular markers capable of evaluating patient risks and advising treatments. This review focuses on recent developments in single-cell molecular analysis of cancer, and its applications for HNSCC diagnosis and treatments. For proof of concept, we examined gene expression levels of 62 patients with HNSCC, and correlate the gene expression profiles to single-cell gene expression profiles obtained from a pilot single-cell study of CCR5-positive breast carcinoma cells. The single-cell molecular analyses complemented the lysate data and reveals heterogeneity of oncogenesis pathways with the cancer cell population. Our single-cell molecular analysis indicated that molecular heterogeneity exists in HNSCC and should be addressed in treatment strategy of HNSCC. Single-cell molecular technology can have significant impact on diagnosis, therapeutic decision making, and prognosis of HNSCC.