Tissue RNA in situ Detection

Spatial Multi-omics Technical Service

Technical Background

In 2022, spatial multi-omics technology was rated as one of the seven “subversive” technologies by Nature. It combines the detection results of genome, transcriptome, proteome and metabolome with the in situ information of tissues to reshape the positioning and distribution of relevant group information in the spatial three-dimensional structure. With the approval of several immunotherapy drugs, tumor immunotherapy is becoming more and more mature, and people pay more and more attention to the concept of tissue microenvironment and its role in tumor immunotherapy. Tumor cells can affect the surrounding microenvironment by releasing extracellular signals, promote tumor angiogenesis and inhibit the surrounding immune cells, and the immune cells and factors in the tumor microenvironment can affect the growth of tumor cells (see the figure below).

Service Content

A wide range of spatial multi-omics technologies are available, from high-throughput spatial transcriptomics and proteomics to low-throughput RNA in situ detection techniques and multiplex fluorescence immunohistochemistry (IHC) methods. Each of these methodologies plays a unique role in its respective application area. PhenoVision Bio Co., Itd possesses an advanced spatial multi-omics platform and downstream data analysis platform. Backed by a team of experienced professionals with years of expertise in this field, PhenoVision integrates R&D, laboratory services, and clinical application development. It offers a one-stop spatial biology solution for multi-omics (DNA, RNA, and protein) analysis.

Show case

Human Endometrial cancer FFPE sections stained with CD3, CD45RO, panCK, CD56 and CD8.

mIF Image Data Analysis Service

Introduction to the analytics platform

The rapid development of multicolor immunofluorescence (mIF) technology has made it possible to decode more secrets of the tissue microenvironment in situ at the single-cell level. After obtaining high-quality mIF staining results, the next important question is how to decipher and interpret these results. By introducing Visiopharm’s leading image analysis artificial intelligence algorithm, Finovicam has generated the “Pheno whole slide analysis (PWSA) platform” to mine the rich data information of whole slide scan images, so as to deeply interpret the tissue microenvironment and spatial biology information.

Basic image analysis—
Cell protein phenotypic analysis
detection of the number, percentage and density of positive cells (single positive, double positive, multiple positive) of marked proteins

In-depth image analysis

Tissue RNA in situ Detection

Technical background

The RNAscope technology developed by ACDbio is a new generation of RNA in situ hybridization (ISH) technology, which has significant advantages over traditional RNA ISH techniques. Part of technical professionals of Phenovision Bio Co., Itd technical team had been working in ACD China Research and development team, and are proficient in the entire technical process from section staining to image acquisition and result interpretation. Combining a professional image analysis team, Phenovision ‘s RNA in situ detection service team conducts in-depth analysis and interpretation of the image results of RNA ISH, and performs in-depth data mining to provide more multidimensional output of results.

Show cases

Analysis of RNA ISH image data

Introduction

RNA in situ hybridization (RNA ISH) is a technique to detect the expression and localization of specific RNAs at the tissue or cell level. It uses labeled nucleic acid probes to hybridize specifically with the RNA to be detected, and determines the location and abundance of RNA by detecting markers, which can be used in research fields such as gene expression and disease diagnosis, with accurate localization and can be combined with morphological analysis. The RNAscope technology developed by ACD is a new generation of RNA in situ hybridization technology, which has obvious advantages over traditional RNA in situ hybridization technology.

By introducing Visiopharm’s leading image analysis artificial intelligence algorithm, Finovicam has generated the “Pheno whole slide analysis (PWSA) platform”, which can analyze the scanned images after RNA ISH staining and mine the rich data information of the images.

Show case 1

The name of the analysis		Analyze the content
Pathological annotation	Pathological zoning	In the scan image, the pathological area is divided
Pathological annotation	ROI regionalization	There is a need to divide multiple ROI regions in a single slice

a. After RNA ISH detection (coronal section of rat brain), the corresponding pathological area was circled

Show case 2

b. After RNA ISH detection (liver tissue), the ROI region was circled

c. Identify all positive RNA signal points in the RNA ISH detection result graph, divided into red (<1.5 μm2), yellow (1.5 μm2- 5 μm2), pink (≥5 μm2) according to the area of the positive RNA signal points

Show case 3

d. Identification of all positive RNA signal points in the image of the RNA ISH assay result (left) (right, tricolor pseudocolor marking)

Note: The transcriptional differences between the hippocampus and substantia nigra regions of Rn-Crem in the control group (i) and the experimental group (l) were detected in coronal sections of the rat brain (the red signal points in the i and l figures are the positive signal of the RNA).

Figures g and m are all positive RNA signal points in the identified detection result pictures (red pseudocolor marking); Figures k and n are identified for all the cells.

The tissue area, number of cells, number of RNA positive signals, density of signal spots, and average number of RNA signal points per cell are given in each rat sample.

Show Case 4

Caption: Detection of cerebral cortex RNA expression in coronal sections of mouse brain.

Figure f shows a partial enlarged picture of the scanned part of the detection result (40x, yellow, pink and green signal points are the positive signals of three different RNA probes, respectively); Figure g shows all positive RNA signal points in the identified f-panel; Figure h shows all types of RNA-positive cells identified in the area of figure f.

The table shows the tissue area of the cerebral cortex on both sides, the total number of cells, the number of cells labeled with different RNA combinations, the positive rate, and the cell density in each sample.

Show Case 5

Caption: RNAscope (pink, red, and yellow fluorescein labeled three RNA signals: marker 4, 5, 6) + mIHC/mIF (smoke blue, green, and white fluorescein labeled three protein signals: marker 1, 2, and 3) were performed in human lung cancer samples, and the data analysis of the whole scans was performed.

Fig. o shows a region of the original scan image (20x) of the whole section;

Figures P and Q show the identification of tumor parenchyma and tumor stromal regions, as well as the identification of different types of positive cells in tumor parenchyma and stroma (see the gray area in the table for analysis results).

Figures R and S are the identification of 0-25 μm (pink pseudo-color), 25-50 μm (red pseudo-color), 0-25 μm (green pseudo-color), 25-50 μm (yellow pseudo-color) and internal tumor tissue (white pseudo-color) outside the tumor tissue boundary, as well as the identification of specific marker combinations of cells in different recognition regions (see the blue area section in the table for analysis results).

Figures T and U are the identification of a certain class of positive cells of interest in this study, and the tissue regions in the three gradient ranges of 0-25 μm, 25-50 μm, and 50-75 μm around these cells in the tumor parenchymal region are labeled as green, red, and yellow pseudocolors, as well as the identification of other specific marker combinations within these three gradients (see the green area section of the table for analysis results).

Organoid paraffin sample preparation

Technical background

Organoid samples can simulate the morphology and function of human tissues, organs, or tumors, and can be obtained through in vitro culture. They also possess a variety of complex cellular structures and are increasingly drawing attention in the field of biology. In December 2021, the Center for Drug Evaluation (CDE) of China released the document “Technical Guidance for Nonclinical Research and Evaluation of Gene Therapy Products (Trial),” which explicitly stated that organoids can serve as an alternative model to animal models in nonclinical studies. In the document “Technical Guidance for Nonclinical Research of Gene-Modified Cell Therapy Products (Trial),” it was also pointed out that organoids can be used as models for evaluating efficacy and safety. Recently, the FDA announced that it will no longer require animal testing as a prerequisite for new drug approval. It has also approved a new drug (NCT04658472) that obtained preclinical data entirely based on “organ-on-a-chip” research to enter clinical trials. This milestone event signifies that “organ-on-a-chip” experiments have replaced traditional animal experiments and have been officially recognized by regulatory authorities.

Service Content

Organoids are small in size and cannot easily undergo the standard tissue embedding process like conventional tissue samples. They are highly prone to being lost during the embedding process due to their small volume. Even if successfully embedded in paraffin blocks, they are often difficult to locate and prepare sections because of their small size. PhenoVision Bio Co., Ltd. has introduced advanced micro-sample preparation experience from abroad and the CellGel sample processing gel. This allows the organoid samples to be fixed in a localized area, which can be clearly and easily located by the naked eye, facilitating subsequent paraffin sectioning and corresponding detection.

The left image shows the location of the CellGel block containing the organoids after CellGel treatment within the paraffin block. The right image is a section of this paraffin block, where the circular area in each wax slice is the CellGel, containing several organoids.

Direction of application

In the “Chinese Expert Consensus on Quality Control Standards for Tumor Organoid Diagnosis and Treatment Platforms (2022 Edition),” published in the Chinese Journal of Cancer in 2022, it is clearly stated that the identification of tumor organoids is an essential prerequisite for drug sensitivity testing. It is recommended to use histopathological methods to identify tumor organoids to ensure the reliability of subsequent drug sensitivity test results.

By performing histopathological staining on organoids, it can be observed that the organoids have a regular morphology and largely retain the morphological characteristics of the donor tissue. Moreover, since organoids can well preserve the heterogeneity of the donor tissue, they can be used for drug sensitivity testing and for formulating personalized treatment plans for patients.
At the same time, preparing organoid samples into paraffin-embedded tissues also facilitates long-term sample storage. The storage cost is much lower than that of -80°C cryopreservation, which is conducive to establishing organoid biobanks and conducting retrospective studies.

Case Show

Gastrointestinal Organoid Paraffin Section Multiplex Immunofluorescence Detection

Tumor Organoid Paraffin Section Multiplex Immunofluorescence Detection

Organoid Paraffin Section H&E Staining

Organoid Paraffin Section RNA ISH Detection (RNAscope Method)

Histopathology Technical Service

Service Content

Provide comprehensive histopathology technical services, including paraffin embedding of pathology samples, section preparation, tissue microarray (TMA) construction, staining (H&E, IHC, FISH, etc.), pathological slide review, and data analysis.
In addition to routine samples, we also have extensive experience in handling difficult samples encountered in clinical and research settings, such as bone tissue, nerve tissue, and micro-samples.