The Immunity Deciphering Project
Introduction
Immunity refers to the comprehensive ability of the human body to resist pathogenic invasion, clear antigenic foreign substances, monitor and maintain the homeostasis of the body. The enormous diversity and complexity of the immune system are the key foundations of immunity, and traditional research models have failed to systematically reveal the generation and evolution of immunity. This major research plan aims to elucidate the scientific connotations of immunity from multiple perspectives, comprehensively understand the operating mechanisms of the human immune system in health and disease states, based on a holistic concept and a complex systems perspective. By integrating theories and technologies from mathematics, information science, life science, and medicine, this plan aims to decipher the immunity through a new research paradigm, promote precision diagnosis and treatment, and serve the “Healthy China” strategy.
Scientific Objectives
Based on high-quality, standardized immunological big data, conduct comprehensive and systematic digital analysis and reconstruction of human immunity, reveal the biological basis of immunity, the key features of immune maintenance, and the universal laws of immune regulation, thereby elucidating the scientific connotations of immunity. Quantify and digitize immune characteristics, establish population immune feature maps, analyze the relationship between immunity and major life events such as aging and disease, and elucidate their underlying mechanisms and laws. Develop key technologies such as disease risk warning, immune visualization, and immune age determination based on immunity data, establish individual and population immunity profiles, carry out health maintenance and disease prevention strategies based on immune intervention, and form a new model of prevention before illness, early disease diagnosis, prognosis assessment, personalized medical care, and health management.
Core Scientific Questions
(1) The Material Basis and Dynamic Laws of the Complex System of Immunity
Given the highly diverse, spatiotemporally dynamic, multidimensional interactive, autonomous adaptive, and pattern memory characteristics of the immune system, employing a dynamic, multiscale, multilevel panoramic research paradigm, integrating model-driven and data-driven research methods, to elucidate the spatiotemporal dynamic characteristics of the complex immune system, clarify the biological connotations of the constituents, intrinsic connections, and variation laws of immunity, and reveal the essential laws and deep operational mechanisms of immunity.
(2) Multimodal Quantitative Characterization and Digital Presentation of Immunity
Establishing population cohorts in different states of immune characterization, acquiring and analyzing diverse information of immune molecules and cells, systematically analyzing immune data at different scales such as molecular, cellular, intercellular correlations, organ, and population, forming standardized multidimensional immune big data sets. Couple, reconstruct, and panoramic characterize multi-source, high-dimensional and multiscale immune data to achieve digital presentation and quantitative evaluation of immunity, and accurately portraying the immune portrait.
(3) Disease Diagnosis, Treatment, and Health Assessment Based on Immune Decoding
Elucidating the immune characteristics and evolutionary laws of major life events in health, obtaining immune signals before organic lesions of major diseases, establishing disease molecular subtyping, precision diagnosis, and prognosis evaluation standards based on immune decoding, integrating traditional medical syndrome identification and concepts such as “preventing illness before it occurs”, constructing disease warning systems, timely assessing health status, and discovering early hidden dangers of diseases, providing theoretical basis for immune intervention in critical life processes.
Given the highly diverse, spatiotemporally dynamic, multidimensional interactive, autonomous adaptive, and pattern memory characteristics of the immune system, employing a dynamic, multiscale, multilevel panoramic research paradigm, integrating model-driven and data-driven research methods, to elucidate the spatiotemporal dynamic characteristics of the complex immune system, clarify the biological connotations of the constituents, intrinsic connections, and variation laws of immunity, and reveal the essential laws and deep operational mechanisms of immunity.
(2) Multimodal Quantitative Characterization and Digital Presentation of Immunity
Establishing population cohorts in different states of immune characterization, acquiring and analyzing diverse information of immune molecules and cells, systematically analyzing immune data at different scales such as molecular, cellular, intercellular correlations, organ, and population, forming standardized multidimensional immune big data sets. Couple, reconstruct, and panoramic characterize multi-source, high-dimensional and multiscale immune data to achieve digital presentation and quantitative evaluation of immunity, and accurately portraying the immune portrait.
(3) Disease Diagnosis, Treatment, and Health Assessment Based on Immune Decoding
Elucidating the immune characteristics and evolutionary laws of major life events in health, obtaining immune signals before organic lesions of major diseases, establishing disease molecular subtyping, precision diagnosis, and prognosis evaluation standards based on immune decoding, integrating traditional medical syndrome identification and concepts such as “preventing illness before it occurs”, constructing disease warning systems, timely assessing health status, and discovering early hidden dangers of diseases, providing theoretical basis for immune intervention in critical life processes.
Scope
The major research plan aims to focus on major diseases with high incidence/high mortality rates in China, such as malignant tumors and autoimmune diseases, as well as healthy populations. Malignant tumors include high-incidence/high-mortality tumors such as lung cancer, liver cancer, colon cancer, pancreatic cancer, etc., and autoimmune diseases include high-incidence types such as systemic lupus erythematosus, rheumatoid arthritis, etc. Healthy populations include different age groups (such as infants, adolescents, adults, elderly, etc.), different health states (such as longevity, traditional Chinese medicine constitution, etc.), or populations in specific living environments.
Data Standards
All generated cohort data must have complete individual clinical information and matched high-throughput omics data. Individual clinical information includes individual health examination information, disease diagnosis, treatment, prognosis, and follow-up; high-throughput omics sequencing data are subdivided into basic type data and expanded type data. Project leaders must provide basic type data for all samples, and project leaders are encouraged to use innovative immune feature detection technologies to generate expanded type data according to actual conditions, thereby characterizing immune status from multiple dimensions. Project proposals must describe the number of samples and annual data generation and submission plans. The sample size and the data type coverage of basic and expanded data will be included in the evaluation and assessment criteria.
Sample Collection Requirements
Tumor samples must include tumor tissue, adjacent normal tissue, and blood samples. Samples must have accurate pathological information, including diagnosis, treatment, prognosis, and follow-up information, and must be equipped with pathology slides stained with H&E, with fresh tissue extracted for cell library construction. Samples must have frozen tissue, and if there are remaining samples, they must be frozen for later experimental verification. Samples of other diseases such as autoimmune diseases should be sampled according to the characteristics of the disease and the detection methods, and specific requirements can refer to tumor samples. The requirements are as follows:
Sample Collection Standards
Tissue specimens: Tissue specimens should preferably remove components such as necrotic tissue, blood stains, etc., which may affect the test results. Preliminary processing such as OCT embedding, formalin fixation, liquid nitrogen quick freezing, tissue dissociation, etc., should be carried out according to different research purposes, and the above steps should be completed within 60 minutes after the sample is removed from the body. Tumor specimens should include non-necrotic tumor tissue and adjacent normal tissue. Other types of tissue specimens such as lymph nodes should select representative areas of lesion tissue. The quality of tissue specimens should meet at least the experimental requirements of basic type data;
Peripheral blood samples: Extract an appropriate amount of peripheral venous blood samples according to different experimental requirements, and the collection volume must meet the experimental requirements of basic type data. Depending on the experimental needs, samples should be centrifuged, frozen, etc., and plasma or serum should be extracted within 30 minutes and stored at -80°C. Other corresponding operations such as immune cell sorting should be completed within 12 hours after extraction.
Quality Control of Samples
Tumor tissue: The mass of tumor tissue should not be less than 0.5 grams, the purity of tumor samples (proportion of tumor cell nuclei) should not be less than 60%, and the necrosis rate of tumor tissue should be less than 20%. The volume of whole blood samples should be greater than 5 milliliters. Pathological slides of tumor tissue should be available to accurately determine the types and histological characteristics of tumor tissue and adjacent normal tissue. Other tissue requirements refer to the quality control standards of tumor tissue;
Blood samples: There should be no coagulation or hemolysis, and if immune cell sorting is performed, the number of PBMCs separated per case should be greater than 8×106, the number of T cells should be greater than 3×106, the number of B cells should be greater than 1×106, and the cell viability should be greater than 95%.
Sample Transportation
Use ice packs, dry ice, or liquid nitrogen for the transportation of tissue samples and peripheral blood samples.
Sample Preservation
Store frozen at -80°C or in liquid nitrogen, must be packaged and stored separately, and should not be thawed and refrozen more than once.
Samples must specify detailed clinical pathological information, with specific requirements as follows:
Traceable unique ID of the subject (such as ID card number, hospitalization number, etc.);
Pathological diagnosis, diagnosis time, pathological type, pathological staging (such as TNM staging), and pathology slide number of the subjects;
Sample collection details including time and location, method of sample acquisition (biopsy/surgical excision, etc.), method of sample preservation (frozen tissue, frozen sections, paraffin sections, etc.), sample volume (grams, milliliters), sample purity (percentage of tumor cell nuclei, etc.), tissue necrosis rate;
Names of tests conducted on tissue and blood samples, testing technology pathways, quality control reports (if available).
Clinical Information Data Types
Healthy Population: Clinical data including but not limited to unique identifiable ID, gender, age, ethnicity, race, region, blood type, height, weight, vaccination history, allergy history, family history of malignant tumors, family history of immune diseases, history of infectious diseases, surgical history, medication history, smoking history, alcohol consumption history.
Autoimmune diseases and malignant tumors: In addition to the data collected for the healthy population, it should also include but not limited to: laboratory tests (at initial diagnosis), imaging examinations, pathological examinations, pathological reports, treatment status, and prognosis.
High-Throughput Omics Data Types
Basic Data
Peripheral Blood:TCR-Seq, BCR-Seq, single-cell multi-omics detection technologies (scRNA-seq, scATAC-seq, scTCR-seq, scBCR-seq).
Tissue Samples:TCR-Seq, BCR-Seq, WES (including tissue samples and corresponding individual peripheral blood), RNA-Seq, single-cell multi-omics detection technologies (scRNA-seq, scATAC-seq, scTCR-seq, scBCR-seq).
Expanded Data
Including but not limited to: mass cytometry (CyTOF), multiplex fluorescence flow cytometry, multiplex immunohistochemistry/immunofluorescence (such as mIHC/CyCIF/mIF/CODEX), proteomics, epigenomics, metabolomics, microbiomics, single-cell spatial transcriptomics, full-length transcriptomics, whole-genome DNA methylation, cfDNA methylation, cfDNA, cfRNA, etc.
Basic Data Quality Control Standards
Bulk TCR/BCR sequencing data standards: The average number of clonotypes in peripheral blood single samples should not be less than 300,000, and in tissue samples, it should not be less than 100,000. The sequencing volume per sample should not be less than 2 Gbp (where effective clonotype is defined as: capable of assembling full-length amino acid sequences and does not contain stop codons, with clear embryonic V and J gene ID information, and the CDR3 region has typical conserved sequences).
Bulk RNA-Seq sequencing standards: DV200 should not be less than 50%, and the coverage should not be less than 50M reads.
WES sequencing standards: Single-base coverage should not be less than 100X, and panel coverage should not be less than 32MB.
Single-cell detection technology indicators
scRNA-seq: Sequencing indicators (effective barcodes not less than 75%, effective UMIs not less than 75%, RNA read Q30 not less than 65%); Cell indicators (not less than 5000 cells per sample, average sequencing read pairs per cell greater than 20,000, median number of genes per cell greater than 1000).
scTCR-seq/scBCR-seq: The average number of VDJ library read pairs per cell should not be less than 5000, RNA read Q30 not less than 75%, effective barcodes not less than 75%; Cell indicators (3000-80000 cells/chip).
scATAC-seq: Sequencing indicators (sequencing read pairs not less than 25000, effective barcodes not less than 75%, barcode Q30 greater than 65%, read1/2 Q30 greater than 65%, sample index Q30 greater than 90%); Cell indicators (not less than 5000 cells per sample, high-quality sequencing fragments greater than 40%).
Other Requirements
Applicants must strictly comply with relevant regulations such as the “Biosafety Law of the People’s Republic of China” and the “Regulations on the Management of Human Genetic Resources of the People’s Republic of China”, and strictly adhere to norms such as medical ethics and patient informed consent. The project proposal involving human genetic resources research should provide the review certificate of the ethics committee of the applicant’s unit or superior authority (scanned copies for electronic applications), otherwise it will not be accepted.
Applicants must specify in the application and task documents whether the provided data meet the standards required in the annex. Otherwise, it will not be accepted.
Projects receiving funding must collect relevant scientific data according to the requirements of the annex and upload the original data to the data center designated by this program, and use it as an important basis for annual assessment.
IDP Standards
Terminology Standards
Domain Ontology Standards
Data Security Management
Classification and Categorization Standards
Contact Information