Gene expression analysis in microdissected renal tissue - Current challenges and strategies

The architecture and compartmentalization of the kidney has stimulated the development of an array of microtechniques to study the functional differences between the distinct nephron segments. With the vast amounts of genomic sequence data now available, the groundwork has been laid for a comprehensive characterization of the molecular pathways defining the differences in nephron function. With the development of sensitive gene expression techniques the tools for a comprehensive molecular analysis of specific renal microenvironments have been provided: Quantitative RT-PCR technologies now allow the analysis of specific mRNAs from as little as single microdissected renal cells. A more global view of gene expression regulation is a logical development from the application of large scale profiling techniques. In this review, we will discuss the power and pitfalls of these approaches, including their potential for the functional characterization of nephron heterogeneity and diagnostic application in renal disease. Copyright (C) 2002 S. Karger AG, Basel

An optimised protocol for isolation of RNA from small sections of laser-capture microdissected FFPE tissue amenable for next-generation sequencing

Background: Formalin-fixed paraffin embedded (FFPE) tissue constitutes a vast treasury of samples for biomedical research. Thus far however, extraction of RNA from FFPE tissue has proved challenging due to chemical RNA–protein crosslinking and RNA fragmentation, both of which heavily impact on RNA quantity and quality for downstream analysis. With very small sample sizes, e.g. when performing Laser-capture microdissection (LCM) to isolate specific subpopulations of cells, recovery of sufficient RNA for analysis with reverse-transcription quantitative PCR (RT-qPCR) or next-generation sequencing (NGS) becomes very cumbersome and difficult. Methods: We excised matched cancer-associated stroma (CAS) and normal stroma from clinical specimen of FFPE canine mammary tumours using LCM, and compared the commonly used protease-based RNA isolation procedure with an adapted novel technique that additionally incorporates a focused ultrasonication step. Results: We successfully adapted a protocol that uses focused ultrasonication to isolate RNA from small amounts of deparaffinised, stained, clinical LCM samples. Using this approach, we found that total RNA yields could be increased by 8- to 12-fold compared to a commonly used protease-based extraction technique. Surprisingly, RNA extracted using this new approach was qualitatively at least equal if not superior compared to the old approach, as Cq values in RT-qPCR were on average 2.3-fold lower using the new method. Finally, we demonstrate that RNA extracted using the new method performs comparably in NGS as well. Conclusions: We present a successful isolation protocol for extraction of RNA from difficult and limiting FFPE tissue samples that enables successful analysis of small sections of clinically relevant specimen. The possibility to study gene expression signatures in specific small sections of archival FFPE tissue, which often entail large amounts of highly relevant clinical follow-up data, unlocks a new dimension of hitherto difficult-to-analyse samples which now become amenable for investigation

Identification of genes for normalization of real-time RT-PCR data in breast carcinomas

<p>Abstract</p> <p>Background</p> <p>Quantitative real-time RT-PCR (RT-qPCR) has become a valuable molecular technique in basic and translational biomedical research, and is emerging as an equally valuable clinical tool. Correlation of inter-sample values requires data normalization, which can be accomplished by various means, the most common of which is normalization to internal, stably expressed, reference genes. Recently, such traditionally utilized reference genes as GAPDH and B2M have been found to be regulated in various circumstances in different tissues, emphasizing the need to identify genes independent of factors influencing the tissue, and that are stably expressed within the experimental milieu. In this study, we identified genes for normalization of RT-qPCR data for invasive breast cancer (IBC), with special emphasis on estrogen receptor positive (ER+) IBC, but also examined their applicability to ER- IBC, normal breast tissue and breast cancer cell lines.</p> <p>Methods</p> <p>The reference genes investigated by qRT-PCR were RPLP0, TBP, PUM1, ACTB, GUS-B, ABL1, GAPDH and B2M. Biopsies of 18 surgically-excised tissue specimens (11 ER+ IBCs, 4 ER- IBCs, 3 normal breast tissues) and 3 ER+ cell lines were examined and the data analyzed by descriptive statistics, geNorm and NormFinder. In addition, the expression of selected reference genes in laser capture microdissected ER+ IBC cells were compared with that of whole-tissue.</p> <p>Results</p> <p>A group of 3 genes, TBP, RPLP0 and PUM1, were identified for both the combined group of human tissue samples (ER+ and ER- IBC and normal breast tissue) and for the invasive cancer samples (ER+ and ER- IBC) by GeNorm, where NormFinder consistently identified PUM1 at the single best gene for all sample combinations.</p> <p>Conclusion</p> <p>The reference genes of choice when performing RT-qPCR on normal and malignant breast specimens should be either the collected group of 3 genes (TBP, RPLP0 and PUM1) employed as an average, or PUM1 as a single gene.</p

Profiling the Proteome of Cyst Nematode-Induced Syncytia on Tomato Roots

Cyst nematodes are important herbivorous pests in agriculture that obtain nutrients through specialized root structures termed syncytia. Syncytium initiation, development, and functioning are a research focus because syncytia are the primary interface for molecular interactions between the host plant and parasite. The small size and complex development (over approximately two weeks) of syncytia hinder precise analyses, therefore most studies have analyzed the transcriptome of infested whole-root systems or syncytia-containing root segments. Here, we describe an effective procedure to microdissect syncytia induced by Globodera rostochiensis from tomato roots and to analyze the syncytial proteome using mass spectrometry. As little as 15 mm2 of 10-µm-thick sections dissected from 30 syncytia enabled the identification of 100–200 proteins in each sample, indicating that mass-spectrometric methods currently in use achieved acceptable sensitivity for proteome profiling of microscopic samples of plant tissues (approximately 100 µg). Among the identified proteins, 48 were specifically detected in syncytia and 7 in uninfected roots. The occurrence of approximately 50% of these proteins in syncytia was not correlated with transcript abundance estimated by quantitative reverse-transcription PCR analysis. The functional categories of these proteins confirmed that protein turnover, stress responses, and intracellular trafficking are important components of the proteome dynamics of developing syncytia

Microarray analysis of GFP-expressing mouse Dopamine neurons isolated by laser capture microdissection

The Central Nervous System (CNS) contains an enormous variety of cell types which organize in complex networks. The lack of adequate markers to discern unequivocally among this cellular heterogeneity make the task of dissecting out such neural networks and the cells that comprise them very challenging. The present study represents a \u201cbottom-up\u201d approach that entails a description of A9 and A10 nuclei, which are components of the mesencephalic dopaminergic system, and the identification of their molecular make-up through microarray analysis of their gene expression profiles. These mesencephalic dopaminergic nuclei give rise to the mesocortical and mesostriatal projections and are well known for their roles in initiation of movement, reward behaviour and neurobiology of addiction. Moreover, in post mortem brains of Parkinson Disease patients a specific topographic pattern of degeneration of these neurons, also recapitulated in experimental animal models, is noted, with A9 neurons presenting with a higher vulnerability to degeneration with respect to A10 cells among which, neuron loss is almost negligible. Molecular differences may be at the basis of this different susceptibility. In this study we have optimized a protocol for laser-assisted microdissection of fluorescent-expressing cells and have taken advantage of a line of transgenic mice TH-GFP/21-31, which express GFP under the TH promoter in all CA cells, to guide laser capture microdissection of A9 and A10 mDA neurons for differential informative cDNA microarray profiling. Results show that our optimized method retains the GFP-fluorescence of DA cells and achieves good tissue morphology visualization. Moreover, RNA of high quality and good reproducibility of hybridizations support the validity of the protocol. Many of the genes that resulted differentially expressed from this analysis were found to be genes previously known to specifically define the different identities of the two DA neuronal nuclei. Transcripts were verified for expression, in DA neurons, using the collection of in situ hybridization in the Allen Brain Atlas. We have identified 592 differentially expressed transcripts (less than 8%) of which 242 showing higher expression in A9 and 350 showing higher expression in A10. Categorical analysis showed that transcripts associated with mitochondria and energy production were enriched in A9, while transcripts involved in redox homeostasis and stress response resulted enriched in A10. Of all the differentially expressed genes, eight transcripts (Mif, Hnt, Ndufa10, Aurka, Cs, enriched in A9 neurons and Pdia5, Whrn, and Gpx3 enriched in A10 neurons), verified with the Allen Brain Atlas and not noted or confirmed as differentially expressed before, emerged from this analysis. These and other selected genes are discussed

New quick method for isolating RNA from laser captured cells stained by immunofluorescent immunohistochemistry; RNA suitable for direct use in fluorogenic TaqMan one-step real-time RT-PCR

We describe a new approach for reliably isolating one-step real-time quantitative RT-PCR-quality RNA from laser captured cells retrieved from frozen sections previously subjected to immunofluorescent immunohistochemistry (IF-IHC) and subsequently subjected to fluorogenic one-step real-time RT-PCR analysis without the need for costly, time-consuming linear amplification. One cell’s worth of RNA can now be interrogated with confidence. This approach represents an amalgam of technologies already offered commercially by Applied Biosystems, Arcturus and Invitrogen. It is the primary focus of this communication to expose the details and execution of an important new LCM RNA isolation technique, but also provide a detailed account of the IF-IHC procedure preceding RNA isolation, and provide information regarding our approach to fluorogenic one-step real-time RT-PCR in general. Experimental results shown here are meant to supplement the primary aim and are not intended to represent a complete scientific study. It is important to mention, that since LCM-RT-PCR is still far less expensive than micro-array analysis, we feel this approach to isolating RNA from LCM samples will be of continuing use to many researchers with limited budgets in the years ahead

MSI-based mapping strategies in tumour-heterogeneity

Since the early 2000s, considerable innovations in MS technology and associated gene sequencing systems have enabled the "-omics" revolution. The data collected from multiple omics research can be combined to gain a better understanding of cancer's biological activity. Breast and ovarian cancer are among the most common cancers worldwide in women. Despite significant advances in diagnosis, treatment, and subtype identification, breast cancer remains the world's second leading cause of cancer-related deaths in women, with ovarian cancer ranking fifth. Tumour heterogeneity is a significant hurdle in cancer patient prognosis, response to therapy, and metastasis. As such, heterogeneity is one of the most significant and clinically relevant areas of cancer research nowadays. Metabolic reprogramming is a hallmark of malignancy that has been widely acknowledged in recent literature. Metabolic heterogeneity in tumours poses a challenge in developing therapies that exploit metabolic vulnerabilities. Consequently, it is crucial to approach tumour heterogeneity with an unlabeled yet spatially specific read-out of metabolic and genetic information. The advantage of DESI-MSI technology originates from its untargeted nature, which allows for the investigation of thousands of component distributions, at a micrometre scale, in a single experiment. Most notably, using a DESI-MSI clustering approach could potentially offer novel insights into metabolism, providing a method to characterise metabolically distinct sub-regions and subsequently delineate the underlying genetic drivers through genomic analyses. Hence, in this study, we aim to map the inter-and intra-tumour metabolic heterogeneity in breast and ovarian cancer by integrating multimodal MSI-based mapping strategies, comprising DESI and MALDI, with IMC (Imaging Mass Cytometry) analysis of the tumour section, using CyTOF, and high- throughput genetic characterisation of metabolically-distinct regions by transcriptomics. The multimodal analysis workflow was initially performed using sequential breast cancer Patient-Derived Xenografts (PDX) models and was expanded on primary tumour sections. Moreover, a newly developed DESI-MSI friendly, hydroxypropyl-methylcellulose and polyvinylpyrrolidone (HPMC/PVP) hydrogel-based embedding was successfully established to allow simultaneous preparation and analysis of numerous fresh frozen core-size biopsies in the same Tissue Microarray (TMA) block for the investigation of tumour heterogeneity. Additionally, a single section strategy was combined with DESI-MSI coupled to Laser Capture Microdissection (LCM) application to integrate gene expression analysis and Liquid Chromatography-Mass Spectrometry (LC-MS) on the same tissue segment. The developed single section methodology was then tested with multi-region collected ovarian tumours. DESI-MSI-guided spatial transcriptomics was performed for co-registration of different omics datasets on the same regions of interest (ROIs). This co-registration of various omics could unravel possible interactions between distinct metabolic profiles and specific genetic drivers that can lead to intra-tumour heterogeneity. Linking all these findings from MSI-based or guided various strategies allows for a transition from a qualitative approach to a conceptual understanding of the architecture of multiple molecular networks responsible for cellular metabolism in tumour heterogeneity.Open Acces

Infrared Laser Ablation for Biomolecule Sampling

In this research, an infrared laser at a wavelength of 3 µm was used to ablate material from tissue sections for biomolecule analysis. Pulsed infrared (IR) irradiation of tissue with a focused laser beam efficiently removed biomolecules, such as proteins, enzymes, DNA, and RNA from tissue sections for further analysis. In a proteomics project, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to determine regions of interest (ROI) for laser ablation. The matrix was then washed off. By overlaying the MSI generated heat-map, the section was sampled using IR laser ablation and custom stage-control software. Two ROI were selected and ablated from the same tissue section after MALDI-MSI. More than 700 proteins were identified in each region. A comparison of molecular localization and activity of identified proteins from two regions was performed. IR laser ablation was used to transfer enzymes while retaining their enzymatic activity. Three different laser fluences were used for ablating two enzymes: trypsin and catalase. Approximately 75% of the enzyme was transferred for all the fluences tested. According to fluorescence quantification, around 35% of the captured trypsin and 51% of the captured catalase were active after laser ablation. Regions were ablated and captured from frontal cortex and cerebellum of rat brain tissue sections and catalase activity was measured from the ablated material without further sample preparation. The catalase activity in the two regions was consistent with previously published data, demonstrating transfer of active enzymes from tissue. IR-laser ablation was used for sampling DNA and RNA. To test ablation transfer of large DNA, a 3200 base pair plasmid was used and evaluation of DNA quality after laser ablation was accomplished by comparing the sequencing performance of samples obtained from laser ablation and a control plasmid. Consistent results for intact DNA were obtained when the laser fluence was below 24 kJ/m2. Regions 1 and 4 mm2 square were ablated from rat brain and kidney tissue sections. Ablated material was amplified using polymerase chain reaction (PCR) with four primers from two genes. For RNA sampling, human kidney total RNA was used. The integrity of the RNA after laser ablation was monitored by gel electrophoresis. Low and high energy thresholds were determined, indicating the range in which intact RNA transfer could be achieved at the highest efficiency. Areas 2 mm2 square were ablated from the rat brain tissue. After RNA purification and reverse transcription, mRNA was amplified and quantified using quantitative PCR with two genes

Application of Laser Microdissection to plant pathogenic and symbiotic interactions

Abstract Laser Microdissection (LM) is a technology that allows the rapid procurement of selected cell populations from a section of heterogeneous tissues in a manner conducive to the extraction of DNA, RNA, proteins and even metabolites. In the past few years, it has also been applied to plant biology in order to study gene expression in plant-nematode and plant-microbe interactions. LM represents a powerful tool since cells associated with a particular infection stage can be visualized under the microscope and harvested. Therefore, verification of the response of the plant during the progression of the colonization can be performed in different cell types. Applications of LM to study the interaction between the plant and both pathogenic and symbiotic organisms (i.e. nematode and fungi, respectively) are explored in this review