Rapid and in vivo quantification of cellular lipids in Chlorella vulgaris using near-infrared Raman spectrometry.

A rapid and noninvasive quantification method for cellular lipids in Chlorella vulgaris is demonstrated in this study. This method applied near-infrared Raman spectroscopy to monitor the change of signal intensities at 1440 cm(-1) and 2845-3107 cm(-1) along the nitrogen depletion period, and calibration curves relating signal intensity and cellular lipid abundance were established. The calibration curves show that signal intensity at 2845-3107 cm(-1) and cellular lipid abundance were highly correlated. When the calibration curve was applied on the lipid quantification of two unknown samples, the differences between lipid abundances estimated by the calibration curve and measured by gas chromatography were less than 2 wt %. Carotenoids produced a strong and broad peak near 1440 cm(-1), and it weakened the correlation between signal intensity and lipid abundance. The consistency of detection and effects of cellular contents and water on the Raman spectrogram of Chlorella vulgaris were also addressed. The sample pretreatment only involved centrifugation, and the time required for lipid quantification was shortened to less than 1.5 h. The rapid detection has great potential in high-throughput screening of microalgae and also provides valuable information for monitoring the quality of microalgae culture and determining parameters for the mass production of biodiesel from microalgae.

Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients.

Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.

Microfluidic techniques for enhancing biofuel and biorefinery industry based on microalgae.

This review presents a critical assessment of emerging microfluidic technologies for the application on biological productions of biofuels and other chemicals from microalgae. Comparisons of cell culture designs for the screening of microalgae strains and growth conditions are provided with three categories: mechanical traps, droplets, or microchambers. Emerging technologies for the in situ characterization of microalgae features and metabolites are also presented and evaluated. Biomass and secondary metabolite productivities obtained at microscale are compared with the values obtained at bulk scale to assess the feasibility of optimizing large-scale operations using microfluidic platforms. The recent studies in microsystems for microalgae pretreatment, fractionation and extraction of metabolites are also reviewed. Finally, comments toward future developments (high-pressure/-temperature process; solvent-resistant devices; omics analysis, including genome/epigenome, proteome, and metabolome; biofilm reactors) of microfluidic techniques for microalgae applications are provided.

Current developments in high-throughput analysis for microalgae cellular contents.

Microalgae have emerged as one of the most promising feedstocks for biofuels and bio-based chemical production. However, due to the lack of effective tools enabling rapid and high-throughput analysis of the content of microalgae biomass, the efficiency of screening and identification of microalgae with desired functional components from the natural environment is usually quite low. Moreover, the real-time monitoring of the production of target components from microalgae is also difficult. Recently, research efforts focusing on overcoming this limitation have started. In this review, the recent development of high-throughput methods for analyzing microalgae cellular contents is summarized. The future prospects and impacts of these detection methods in microalgae-related processing and industries are also addressed.