AbstractLaser desorption/ionization mass spectrometry offers distinct advantages for biomolecular analysis owing to its high salt tolerance, low sample consumption, and soft ionization capability. However, localized laser−sample interactions are inherently affected by heterogeneous distributions of analytes and materials, leading to substantial variation between measurements. Improving signal precision has therefore long been a central pursuit in mass spectrometric analysis for reliable qualitative and quantitative biomolecular information. Herein, we report a laser-induced graphene (LIG) microarray platform for enhancing analytical precision in surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS). A straightforward thermal confinement strategy enabled high-resolution LIG patterning under CO2 laser irradiation. Using an unmodified commercial laser cutter, monolithic porous LIG domains with feature sizes as small as 60 μm, below the MS laser spot diameter, were directly patterned on polyimide films. The wettability contrast between hydrophilic LIG and hydrophobic polyimide guided analyte accumulation onto SALDI-active LIG spots, which were fully interrogated by the MS laser for reproducible desorption/ionization. This integrated control over LIG feature size and analyte localization reduced the relative standard deviation of MS signal intensity from 78.47% on the unconfined substrate to 6.27% across 30 microarray spots. The platform demonstrated applicability to diverse biomolecules in both positive and negative ion modes. Quantitative accuracy was further demonstrated through enzyme kinetic studies, giving kinetic parameters consistent with conventional colorimetric results. More importantly, LIG microarray-assisted SALDI-MS enables direct molecular tracking of colorless native substrate turnover, underscoring the broader potential of nanostructured carbon interfaces for reaction-resolved mass spectrometric analysis.

In this issue, we will review a new study by Dr. Alaa Jamal and Dr.

Spatially resolved transcriptomics was named Method of the Year 2020 and has continued to evolve rapidly since then, providing novel insights in development, physiology, and disease processes. Many ap...

Since the development of DNA microarrays and later RNA bulk sequencing, testing with statistically independent samples has been the standard method for detecting genes with different transcription patterns. Single-cell assays challenge these assumptions because individual cells are statistically dependent, and all proposed methodologies present mathematical limitations or computational bottlenecks that prevent a seamless integration of data from many cells and patients simultaneously. In this work, we solve this crucial limitation by introducing a Bayesian framework that retrieves the independence structure at the level of individual patients, separating differences across individuals from actual transcriptional differences. Leveraging multi-GPU and variational inference, our approach excels across different experimental designs and scales to analyse over 10 million cells. This framework enables single-cell differential expression analysis that can finally integrate datasets from large

The Biochips Microarrays Microfluidics Market is expected to grow from 11.4 USD Billion in 2025 to 25 USD Billion by 2035. The Biochips Microarrays Microfluidics Market CAGR (growth rate) is expected ...

Read the full paper: Chemical Probe Discovery for DEAD-box RNA Binding Protein DDX21 using Small Molecule Microarrays

DOE is easy to teach and its methods are easily retained by students. There’s not much math. My hunch is the DOE was crowded out by more data analysis-heavy courses related to survival analysis, longitudinal data, and especially in the past 25 years, high-dimensional methods for the latest and greatest biomedical tech, starting with gene microarrays, then on to other “omics” data.

DOE is easy to teach and its methods are easily retained by students. There’s not much math. My hunch is the DOE was crowded out by more data analysis-heavy courses related to survival analysis, longitudinal data, and especially in the past 25 years, high-dimensional methods for the latest and greatest biomedical tech, starting with gene microarrays, then on to other “omics” data.

DOE is easy to teach and its methods are easily retained by students. There’s not much math. My hunch is the DOE was crowded out by more data analysis-heavy courses related to survival analysis, longitudinal data, and especially in the past 25 years, high-dimensional methods for the latest and greatest biomedical tech, starting with gene microarrays, then on to other “omics” data.

DOE is easy to teach and its methods are easily retained by students. There’s not much math. My hunch is the DOE was crowded out by more data analysis-heavy courses related to survival analysis, longitudinal data, and especially in the past 25 years, high-dimensional methods for the latest and greatest biomedical tech, starting with gene microarrays, then on to other “omics” data.

DOE is easy to teach and its methods are easily retained by students. There’s not much math. My hunch is the DOE was crowded out by more data analysis-heavy courses related to survival analysis, longitudinal data, and especially in the past 25 years, high-dimensional methods for the latest and greatest biomedical tech, starting with gene microarrays, then on to other “omics” data.

DOE is easy to teach and its methods are easily retained by students. There’s not much math. My hunch is the DOE was crowded out by more data analysis-heavy courses related to survival analysis, longitudinal data, and especially in the past 25 years, high-dimensional methods for the latest and greatest biomedical tech, starting with gene microarrays, then on to other “omics” data.

DOE is easy to teach and its methods are easily retained by students. There’s not much math. My hunch is the DOE was crowded out by more data analysis-heavy courses related to survival analysis, longitudinal data, and especially in the past 25 years, high-dimensional methods for the latest and greatest biomedical tech, starting with gene microarrays, then on to other “omics” data.

Engineering programmable tumor microenvironment interactions through single-cell bioprinting of spatially defined cell microarrays, Helms, Haylie R, Bertassoni, Luiz E

Advanced food sensitivity testing uses CRD MRT microarrays DNA tools for precise personalized dietary interventions

Laser microdissection and microarrays are used to assess 900 precise subdivisions of the brains from three healthy men with 60,000 gene expression probes; the resulting atlas allows comparisons betwee...