Mass spectrometry(MS)based quantification is a powerful tool in biological researches as featuring high sensitivity and throughput in differential protein determination across different pathology/physiology conditions.Previously,we have developed a Fast-seq approach for efficient proteome profiling,and catTFRE strategy for low abundant transcription factors screening.These tools allow us to dissect a proteome with extreme depth and breadth.After solving proteome identification issues,precise quantification became a major challenge in proteomics platform.Extracted peptide ion chromatogram(XIC,MS1 mode)and multiple reaction monitoring(MRM,MS2 mode)are two approaches employed in quantitative proteomics.The mass spectrometry signal response of different tryptic peptides from the same protein,and similarly,different fragment ions from the same peptide,can vary up to 100-fold in intensity.Key to a successful MS1 and MS2 type of quantification is the selection of "best-peptides/transitions-responders" that have decent MS response intensity and wide dynamic range,as it determines the accuracy and sensitivity of the assay.However,even though scientists in Proteomic and MS field have realized this critical issue for a while,because of the difficulties in "Best-responderscreening and lack of related database,people chose to keep silence and accepted iBAQ algorithm,an obvious compromise approach in protein quantification.Although the application of iBAQ in proteomic quantification can relief the awkward of no proper MS quantification method,in long term,it do harm to the progress of proteomics as it has inevitable false quantified rate and cannot be applied in single protein/pathway accurate quantification,especially in clinical researches.We recently generated original data by employing Fast-seq and catTFRE approaches to built SCRIPT-MAP,an experimental database of linear MS response curve of global peptide-transition ions in mammalian proteome.The intensity and dynamic range of over 800,000 transitions from 80,000 peptides(represent almost 9,000 gene products)in serial dilution experiment set were evaluated and presented in SCRIPT-MAP.Low abundant sub-proteomes,such as transcription factors and co-regulators were surveyed by using affinity-based MS strategy.With this experimental dataset,we can easily select and design targeted "bestpeptide/transition-responders" for MS1 or MS2 based quantification by searching SCRIPT-MAP with either pathway or gene/protein list.We hope the combination of Fast-seq,catTFRE and SCRIPT-MAP will provide a reliable identification/quantification solution and eventually facilitate us in better understanding of "driver pathways" in clinical cancer research.