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CIBERSORTx Immune Cell Infiltration Analysis Tool edu Email Registration Application Original Tutorial

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Preface Introduction

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Tool features

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Registration process


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Account verification

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Audit successful

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Easy to use

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Email retrieval

Preface Introduction

CIBERSORT was developed by a research team at Stanford University, using deconvolution algorithms to estimate the composition and abundance of immune cells in mixed cells based on transcriptome data. It has been cited nearly a thousand times so far. The first version of CIBERSORT was published in Nature Methods in 2015, and the current upgraded version of CIBERSORT was published in Nature Biotechnology in 2019.


CIBERSORT is a web-based tool that uses the principle of linear support vector regression to deconvolve the expression matrix of human leukocyte subtypes. Commonly used for chip expression matrices, the deconvolution analysis of unknown mixtures and expression matrices containing similar cell types is superior to other methods (LLSR, LLSR,PERT,RLR,MMAD,DSA) 。 This method is still based on a known reference set and provides a gene expression feature set of 22 subtypes of white blood cells – LM22 Website link: http://cibersort.stanford.edu/

CIBERSORT is a tool based on the principle of linear support vector regression to deconvolve the expression matrix of immune cell subtypes, and RNA Seq data can be used to estimate immune cell infiltration. Users only need to register one account to get 500MB of storage space for data and results. When operating, simply upload the standard expression matrix file to analyze immune infiltration; If you want to analyze the infiltration ratio containing other cell types, you need to upload the corresponding file in the format indicated on the official website.

Tool features

Single cell RNA sequencing has become a powerful technology in modern medical research, allowing scientists to study the expression and behavior of individual cells, such as in diseases like cancer. However, this technology is not yet applicable to preserved tissue samples and is expensive, making it unsuitable for large-scale clinical routine testing.


To address these drawbacks, researchers at Stanford University School of Medicine have invented a computing software called CIBERSORTx that can directly analyze gene expression in individual cells from whole tissue samples or datasets.

Clarify cell types and states
CIBERSORT has made a big leap forward based on the software CIBERSORT developed by the team before. Alizadeh said, ‘In the original CIBERSORT version, it was possible to analyze the frequency of specific molecules in a group of cells and tell us which cells were in this group without the need for physical cell separation.’. We can make an analogy, it’s like analyzing a fruit milkshake, “Newman said.” You can’t see what fruit is in the milkshake, but you can taste it and know that there are many apples, a little banana, and some red strawberries inside. CIBERSORTx further adopted this principle, where researchers first performed single-cell RNA analysis on a small amount of tissue, such as tumor tissue. They separated tumor cells and carefully observed the RNA (as well as proteins) produced by each cell. Through this process, the RNA expression pattern of the cell type can be obtained: a “barcode” that can not only identify the cell type, but also the subtype or working mode it belongs to. For example, when the same immune cells infiltrate a tumor, they produce different RNA and proteins, resulting in RNA barcodes that are different from those in peripheral blood. What CIBERSORTx does is not only let us know how many apples are in a milkshake, but also tell us how many are small green apples, how many are red apples, how many are green apples, and how many are purple apples, “Alizadeh said.” Similarly, starting from the mixed RNA barcodes in tumors can give us a deeper understanding of the cell types and the status of affected cells in these tumors, as well as how we can address these defects for cancer treatment. Scientists say that this tool can not only identify cell types, but also recognize the state or behavior of cells in specific environments, thereby discovering new mechanisms of action and improving treatment methods.

The research team analyzed over 1000 tumor samples using this technique and not only found that the expected cancer cells were different from normal cells, but also found that the immune cells infiltrating the tumor had different effects from circulating immune cells, and even the normal structural cells around the cancer cells were different from the same type of cells in other parts of the organ.

 

Cancer cells are changing all other cells in the tumor, “Newman said. Researchers have even found significant differences in the infiltration of the same immune cells into different types of lung cancer.

The main advantage of CIBERSORTx is that it can be applied to FFPE tissue samples (the vast majority of tumor sample types). Most FFPE samples cannot be analyzed by single-cell RNA sequencing because cell membranes are disrupted or cells cannot separate from each other, making single-cell RNA analysis impractical or impossible for most large-scale studies and clinical trials.

Predicting treatment responseThe researchers also tested the diagnostic ability of the tool by analyzing melanoma. For metastatic melanoma or other cancers, using drugs to block PD-1 and CTLA4 proteins that invade T cells is one of the most effective treatment methods. However, these “checkpoint inhibitors” drugs are only effective in a small number of patients, and there is no easy way to determine which patients will respond.
The previous hypothesis suggested that if a patient’s infiltrating T cells had high levels of PD-1 and CTLA4, these drugs were more likely to work, but researchers have difficulty determining whether this is true. CIBERSORTx can investigate this issue. After algorithm training on single-cell RNA data from a small number of melanoma samples, researchers analyzed previously publicly available melanoma tumor tissue and test fixed sample datasets. They confirmed this hypothesis and found that high expression of PD-1 and CTLA4 in certain T cells is associated with reduced mortality in patients treated with PD-1 blocking drugs. Researchers say that CIBERSORT may also help discover new cellular markers and provide pathways for cancer treatment. Using this tool to analyze saved tissue samples and correlate cell types with clinical outcomes may reveal genes and proteins that are important for cancer growth. It took 30 years to discover PD-1 and CTLA4 as important target proteins, but when CIBERSORTx was used to correlate tumor cell gene expression with treatment outcomes, these markers immediately jumped out, “Alizadeh said. We have seen so many new molecules that may prove to be interesting, “Newman said.” This is a treasure trove.

Registration process

CIBERSORTThe database requires a non-profit organization’s edu education email to register. We need to open the registration address https://cibersort.stanford.edu/register.php

As shown in the following figure:

For Non-Commercial use only. Non-academic and commercial users should gohere. If you are a member of an academic or non-commercial organization, please use your organization’s email. Personal (e.g. Gmail, Yahoo, Hotmail, etc.) and commercial emails ending in .com will be automatically rejected.

For non-commercial use only. Non academic and business users should gohere. If you are a member of an academic or non-commercial organization, please use your organization’s email. Personal emails ending in. com (such as Gmail, Yahoo, Hotmail, etc.) and business emails will be automatically rejected.

When we fill in the information:

Name section: First Name * Last Name*

Email section: Email * School name: Organization * Your workplace or research institution to which you belong.

Organization City: City * Street: State or Province * Organization Country: Country*

Account verification

Thank you for registering! Confirmation email has been sent to ljaime4698@xxxx.edu
Please click on the activation link to activate your account. Please note that this link will expire in 1 hour. If your link has expired, you will receive another activation link when you click on the old link.

After we fill out the information and click submit, our edu email will receive a verification email from cibersortx titled “CIBERSORTx Registration Confirmation”.

To activate your account, please click on this link:Activation Link，If clicking on this link does not work, please copy the link below and paste it into your browser。

Audit successful

After verification, the account cannot be used immediately and requires official manual review and approval from cibersortx. Only after the manual review and approval can we log in and use it, and we will also receive a notification email.If we use an edu email in the United States, it will be sent directly to cibersortx in seconds.

Approved: CIBERSORTx Account Activation Request

Your request for CIBERSORTx account activation has been approved. You may now log in to theCIBERSORTx website.

Easy to use

Step one, upload the data as shown in the figure below, clickMenu—Upload files—Add filesUpload txt data, please refer to the example data for the data format.

Step 2Configure parameters, prepare to run, clickMenu—Run CIBERSORTx—2.Impute Cell FractionsThe specific configuration is as follows:

In order to accelerate the running speed, Permutation for significance analysis has been selected 50 times:

 

Step ThreeAfter running for a period of time, you can see the results,Menu—Job ResultsClick on CSV or XLSX to obtain the predicted result, which isModule 1The output data.

Email retrieval

The edumail-vip official purchasing platform used in this tutorial is the American series educational email, which is lifetime use, free of FQ, and can be logged in and used domestically. The platform also helps to register a cibersortx account, making it very convenient and fast.

Edu Education Network Email Registration Application Purchase Price

If you need to purchase an email account to activate the corresponding product, please click here: https://www.eduemailstore.com/product/allusproduct/

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