BioPhotonics Preview - January/February 2024

Virtual Staining of Tissue

The current histological staining workflow requires tedious sample preparation, specialized laboratory infrastructure, and trained histotechnologists. Deep learning techniques create new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining. Computationally, multiple stains can be performed on each tissue, allowing pathologists to get more out of a single tissue section. These stains can be performed using autofluorescence images of unlabeled tissue sections, or with scans of stained H&E stained tissues, which fits into existing workflows, and these stains have been validated by pathologists.

Key Technologies: tissue staining, deep learning

Raman Photothermal Microscopy

Stimulated Raman scattering (SRS) microscopy has shown enormous potential in revealing molecular structures, dynamics, and couplings in complex systems. Yet, the sensitivity of SRS is fundamentally limited to milli-molar level due to the shot noise and the small modulation depth. Additionally the operation of SRS imaging is complicated by cross phase modulation. We recently revisited SRS from the perspective of energy deposition. The SRS pumps molecules to their vibrationally excited states. The thereafter relaxation heats up the surrounding and induces refractive index changes. By probing the refractive index changes with a laser beam, stimulated Raman photothermal (SRP) microscopy is developed, where a >500-fold boost of modulation depth is achieved. Moreover, SRP imaging can be operated with a noisy fiber laser for excitation and a long working distance air condenser for signal collection. In summary, SRP microscopy opens a new way to perform chemical imaging with ultrahigh sensitivity and ease of operation.

Key Technologies: Stimulated Raman scattering, photothermal microscopy

Medical Sensors in fNIRS

Functional near-infrared spectroscopy (fNIRS) is an optical investigative technique in neurology to map a relationship between localized brain activity and a cognitive task, where the signal is derived from the measured changes in oxidation levels of hemoglobin. The basic setup of fNIRS consists of at least one pair of a light source, injecting light into the brain, and a photodetector, detecting the emerging light. This article begins with a high-level discussion of the role fNIRS plays in neuroscience followed by a description of three basic modalities of fNIRS: continuous wave, frequency domain, and time domain. The rest of the article focuses on time-correlated single photon counting (TCSPC) fNIRS, a particular realization of the time domain modality. This section examines engineering requirements imposed on the light source, on the photodetector, and the basic steps in data analysis.

Key Technologies: Functional near-infrared spectroscopy, time-correlated single photon counting

Optical Filters in Raman Spectroscopy

Non-destructive. Portable. Real-time. Extremely discriminating in its analysis. Raman spectroscopy has established itself as a very powerful tool in the identification and quantification of chemicals in dock-to-stock analysis of pharmaceuticals, in-field and in-container identification of hazardous materials, and screening of liquids in airport security, among many other applications. Raman’s unique abilities to distinguish molecular composition is starting to be applied in bio-analytical applications such as endoscopy, cancer detection, bone density and other in vivo and in vitro biomedical applications. However, like the proverbial search for the needle in the haystack, Raman spectroscopy always struggles with the elusive nature of Raman scattered “signal” photons from within the dominantly Rayleigh scattered source background – typically less than one in a million photons exchange vibrational energy with the molecules under test and exhibit the associated Raman wavelength shift. To extract this precious Raman signal from the background, highly wavelength selective optical filters are necessary to provide “more signal, with less background” to the detectors and enable the molecularly unique Raman fingerprint characteristics to be seen.

Key Technologies: Raman spectroscopy, optical filters

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