A5026197693- Cell Image Analysis Techniques
- Advanced Fluorescence Microscopy Techniques
- Image Processing Techniques and Applications
- Single-cell and spatial transcriptomics
- Advanced biosensing and bioanalysis techniques
- Optical Coherence Tomography Applications
- Bacterial Identification and Susceptibility Testing
- Molecular Biology Techniques and Applications
- MicroRNA in disease regulation
Arizona State University
2023-2024
Bacterial counts from native environments, such as soil or the animal gut, often show substantial variability across replicate samples. This heterogeneity is typically attributed to genetic environmental factors. A common approach estimating bacterial populations involves successive dilution and plating, followed by multiplying colony method, however, overestimates in population because it conflates inherent uncertainty drawing a subsample total with sample arising biological origins. In...
This document, SOP002 - Multiplexed Iterative FISH Experimental Protocol, describes the process for in-situ fluorescence labeling of RNA transcripts in cells and tissues using a layered probe design, which allows identity barcoding (MERFISH or similar). protocol also provides option signal amplification Branched DNA [bDNA] amplification. rounds imaging are possible through use readout with cleavable disulfide (S-S) reporter molecule, method that minimal disruption to sample integrity between...
Document Summary:This document, Preparation of Encoding Probes (SOP005), describes the procedure used to produce final encoding probes in multiplexed iterative FISH experiments, from commercially-derived, low-yield yet affordable oligo libraries. To prepare ordered pool into probe set, oligos are amplified using limited-cycle PCR, then again and shortened during in-vitro transcription. We follow amplification steps with reverse transcription convert our product back intended DNA-based, mRNA...
Abstract Biological images captured by microscopes are characterized heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices smoothness constraints making assumptions on...