DNA Fingerprint Analysis of Raman Spectra Captures Global Genomic Alterations in Imatinib-Resistant Chronic Myeloid Leukemia: A Potential Single Assay for Screening Imatinib Resistance

Label-Free Identification of AML1-ETO Positive Acute Myeloid Leukemia Using Single-Cell Raman Spectroscopy

Acute myeloid leukemia (AML) is a malignant hematological tumor disease. Chromosomal abnormality is an independent prognostic factor in AML. AML with t(8:21) (q22; q22)/AML1-ETO (AE) is an independent disease group. In this research, a new method based on Raman spectroscopy is reported for label-free single-cell identification and analysis of AE fusion genes in clinical AML patients. Raman spectroscopy reflects the intrinsic vibration information of molecules in a label-free and non-destructive manner, and the fingerprint Raman spectrum of cells characterizes intracellular molecular types and relative concentration information, so as to realize the identification and molecular metabolism analysis of different kinds of cells. We collected the Raman spectra of bone marrow cells from clinically diagnosed AML M2 patients with and without the AE fusion gene. Through comparison of the average spectra and identification analysis based on multivariate statistical methods such as principal component analysis and linear discriminant analysis, the distinction between AE positive and negative sample cells in M2 AML patients was successfully achieved, and the single-cell identification accuracy was more than 90%. At the same time, the Raman spectra of the two types of cells were analyzed by the multivariate curve resolution alternating least squares decomposition method. It was found that the presence of the AE fusion gene may lead to the metabolic changes of lipid and nucleic acid in AML cells, which was consistent with the results of genomic and metabolomic multi-omics studies. The above results indicate that single-cell Raman spectroscopy has the potential for early identification of AE-positive AML.

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting

Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within E. coli and yeast. The test accuracy for different culture times and cell cycles of E. coli was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of E. coli growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering.