Sequential window acquisition of all theoretical spectra (SWATH) as a typical data-independent acquisition (DIA) strategy is favorable for untargeted metabolomics. It could theoretically acquire product ions of all precursor ions, including precursor ions showing chromatographic peaks of rather poor qualities. However, existing data processing methods present limited capabilities in capturing poor-quality peaks of precursor ions. Thus, although their product ions could be acquired, their precursor ions are absent. Here, we present a new strategy, chromatographic retention behavior-SWATH (CRB-SWATH), that could unbiasedly capture poor-quality peaks and provide high resolutions of multiplexed mass spectroscopy (MS/MS) spectra in SWATH datasets. CRB-SWATH monitors CRBs of SWATH-MS signals under a series of altered elution gradients. As signals of compounds differ from noise by showing CRBs, both the precursor and fragment ions are captured, while ignoring their peak qualities. Moreover, CRB-SWATH offers good chances to resolve highly multiplexed MS/MS spectra in SWATH datasets because precursor ions coeluted in a single elution gradient often present different CRBs. In the untargeted metabolic analysis of Hela cell extracts, CRB-SWATH showed the advantage in exclusively capturing 2645 ions of poorquality peaks (i.e., tiny peaks, discontinuous ion traces, tailing peaks, zigzag peaks, etc.), accounting for 34.4% of all the untargeted precursor ions extracted. Therein, it is noteworthy that among 2116 negative ions detected in hydrophilic interaction liquid chromatography (HILIC) mode, 1284 poor-quality ion peaks (>60%) were exclusively captured by CRB-SWATH. As CRB-SWATH automatically captures a large sum of true ion peaks of poor qualities, extracts MS/MS spectra of high purities, and provides chromatographic retention behaviors of untargeted metabolites for identification and classification, it could be a useful metabolomics tool for understanding biological phenomena better.

Accurate discrimination and classification of unknown species are the basis to predict its characteristics or applications to make correct decisions. However, for biogenic solutions that are ubiquitous in nature and our daily lives, direct determination of their similarities and disparities by their molecular compositions remains a scientific challenge. Here, we explore a standard and visualizable ontology, termed "biogenic solution map", that organizes multifarious classes of biogenic solutions into a map of hierarchical structures. To build the map, a novel 4-dimensional (4D) fingerprinting method based on data-independent acquisition data sets of untargeted metabolomics is developed, enabling accurate characterization of complex biogenic solutions. A generic parameter of metabolic correlation distance, calculated based on averaged similarities between 4D fingerprints of sample groups, is able to define "species", "genus", and "family" of each solution in the map. With the help of the "biogenic solution map", species of unknown biogenic solutions can be explicitly defined. Simultaneously, intrinsic correlations and subtle variations among biogenic solutions in the map are accurately illustrated. Moreover, it is worth mentioning that samples of the same analyte but prepared by alternative protocols may have significantly different metabolic compositions and could be classified into different "genera".