Leming Shi, PhD, is a Professor at Fudan University and the Director of the International Human Phenome Institutes (Shanghai). A world-renowned leader in establishing quality standards for multiomics data, Dr. Shi's work enhances drug discovery and precision medicine. He spearheaded the international MAQC/SEQC consortia and the MAQC Society, pioneered the first suites of certified multiomics reference materials (the Chinese Quartet), and co-founded Chipscreen Biosciences (688321.SH), resulting in two novel marketed drugs (Chidamide and Chiglitazar). His research in multiomics-based precision medicine for breast cancer has significantly improved patient survival. With over 200 highly cited publications, his work has directly shaped FDA guidance and international standards on multiomics. Dr. Shi holds a PhD in computational chemistry and conducted postdoctoral research at the NIH/NCI, which launched a distinguished career spanning academic, industrial, and regulatory settings.
Ensuring Reproducibility of Quantitative Multiomics Data for Regulatory Decision-Making
The rapid advancement of multiomics technologies has generated vast amounts of biological data, yet the field is fundamentally challenged by poor reproducibility, difficulties in validation, and limited translational application. At the core of this crisis lies a systemic and long-overlooked issue in biometrology: the absence of proper calibration.
Current quantitative multiomics profiling is methodologically flawed, as it mistakenly treats raw instrument signals (e.g., fluorescence intensity, read counts) as direct surrogates for the true concentration of target molecules (e.g., mRNA, protein). This approach ignores the essential need for a reliable quantitative relationship between instrument response and true analyte concentration, which must be established using reference materials and calibration curves. The critical shortage of universally applicable multiomics reference materials makes it impossible to systematically calibrate measurements across different batches, platforms, or laboratory conditions. Consequently, reported data values become highly dependent on the measurement environment, where minor variations in instrument model, reagent lot, operator, or even ambient temperature introduce uncontrolled signal biases. These biases manifest as pervasive "batch effects," leading to insurmountable challenges in data integration, poor comparability across studies, and frequently contradictory biological conclusions (Yu Y et al., Genome Biology 2024).
This presentation will systematically dissect the origins and profound impact of this calibration crisis in multiomics. It will highlight the fundamental metrological error of substituting raw signal for concentration and propose a paradigm shift toward ratio-based quantitative profiling using well-characterized reference materials (Zheng Y et al., Nature Biotechnology 2024). This new paradigm of sample-to-reference ratio (SRR), exemplified by the "Chinese Quartet" multiomics reference materials (chinese-quartet.org), enables effective calibration of instrument response. By adopting this approach, we can fundamentally enhance the reliability, reproducibility, and comparability of multiomics data, thereby fostering the robust development and confident regulatory application of the field, as detailed in the Nature Collections (www.nature.com/collections/ibadeahigd).