CaBLAM! A high-contrast bioluminescent Ca2+ indicator derived from an engineered Oplophorus gracilirostris luciferase.

Ca2+ plays many critical roles in cell physiology and biochemistry, leading researchers to develop a number of fluorescent small molecule dyes and genetically encodable probes that optically report changes in Ca2+ concentrations in living cells. Though such fluorescence-based genetically encoded Ca2+ indicators (GECIs) have become a mainstay of modern Ca2+ sensing and imaging, bioluminescence-based GECIs-probes that generate light through oxidation of a small-molecule by a luciferase or photoprotein-have several distinct advantages over their fluorescent counterparts. Bioluminescent tags do not photobleach, do not suffer from nonspecific autofluorescent background, and do not lead to phototoxicity since they do not require the extremely bright extrinsic excitation light typically required for fluorescence imaging, especially with 2-photon microscopy. Current BL GECIs perform poorly relative to fluorescent GECIs, producing small changes in bioluminescence intensity due to high baseline signal at resting Ca2+ concentrations and suboptimal Ca2+ affinities. Here, we describe the development of a new bioluminescent GECI, "CaBLAM," which displays a much higher contrast (dynamic range) than previously described bioluminescent GECIs coupled with a Ca2+ affinity suitable for capturing physiological changes in cytosolic Ca2+ concentration. Derived from a new variant of Oplophorus gracilirostris luciferase with superior in vitro properties and a highly favorable scaffold for insertion of sensor domains, CaBLAM allows for single-cell and subcellular resolution imaging of Ca2+ dynamics at high frame rates in cultured neurons. CaBLAM marks a significant milestone in the GECI timeline, enabling Ca2+ recordings with high spatial and temporal resolution without perturbing cells with intense excitation light.

Aequorea's secrets revealed: New fluorescent proteins with unique properties for bioimaging and biosensing.

Using mRNA sequencing and de novo transcriptome assembly, we identified, cloned, and characterized 9 previously undiscovered fluorescent protein (FP) homologs from Aequorea victoria and a related Aequorea species, with most sequences highly divergent from A. victoria green fluorescent protein (avGFP). Among these FPs are the brightest green fluorescent protein (GFP) homolog yet characterized and a reversibly photochromic FP that responds to UV and blue light. Beyond green emitters, Aequorea species express purple- and blue-pigmented chromoproteins (CPs) with absorbances ranging from green to far-red, including 2 that are photoconvertible. X-ray crystallography revealed that Aequorea CPs contain a chemically novel chromophore with an unexpected crosslink to the main polypeptide chain. Because of the unique attributes of several of these newly discovered FPs, we expect that Aequorea will, once again, give rise to an entirely new generation of useful probes for bioimaging and biosensing.

Machine Learning and Feature Selection Applied to SEER Data to Reliably Assess Thyroid Cancer Prognosis.

Utilizing historical clinical datasets to guide future treatment choices is beneficial for patients and physicians. Machine learning and feature selection algorithms (namely, Fisher's discriminant ratio, Kruskal-Wallis' analysis, and Relief-F) have been combined in this research to analyse a SEER database containing clinical features from de-identified thyroid cancer patients. The data covered 34 unique clinical variables such as patients' age at diagnosis or information regarding lymph nodes, which were employed to build various novel classifiers to distinguish patients that lived for over 10 years since diagnosis, from those who did not survive at least five years. By properly optimizing supervised neural networks, specifically multilayer perceptrons, using data from large groups of thyroid cancer patients (between 6,756 and 20,344 for different models), we demonstrate that unspecialized and existing medical recording can be reliably turned into power of prediction to help doctors make informed and optimized treatment decisions, as distinguishing patients in terms of prognosis has been achieved with 94.5% accuracy. We also envisage the potential of applying our machine learning strategy to other diseases and purposes such as in designing clinical trials for unmasking the maximum benefits and minimizing risks associated with new drug candidates on given populations.

Characterization of an array of honeys of different types and botanical origins through fluorescence emission based on LEDs.

A set of 10 honeys comprising a diverse range of botanical origins have been successfully characterized through fluorescence spectroscopy using inexpensive light-emitting diodes (LEDs) as light sources. It has been proven that each LED-honey combination tested originates a unique emission spectrum, which enables the authentication of every honey, being able to correctly label it with its botanical origin. Furthermore, the analysis was backed up by a mathematical analysis based on partial least square models which led to a correct classification rate of each type of honey of over 95%. Finally, the same approach was followed to analyze rice syrup, which is a common honey adulterant that is challenging to identify when mixed with honey. A LED-dependent and unique fluorescence spectrum was found for the syrup, which presumably qualifies this approach for the design of uncomplicated, fast, and cost-effective quality control and adulteration assessing tools for different types of honey.

Identification of a premature stop codon mutation in the PHGDH gene in severe Neu-Laxova syndrome-evidence for phenotypic variability.

In some cases Neu-Laxova syndrome (NLS) is linked to serine deficiency due to mutations in the phosphoglycerate dehydrogenase (PHGDH) gene. We describe the prenatal and postnatal findings in a fetus with one of the most severe NLS phenotypes described so far, caused by a homozygous nonsense mutation of PHGDH. Serial ultrasound (US) and pre- and postnatal magnetic resonance imaging (MRI) evaluations were performed. Prenatally, serial US evaluations suggested symmetric growth restriction, microcephaly, hypoplasia of the cerebellar vermis, micrognathia, hydrops, shortened limbs, arthrogryposis, and talipes equinovarus. The prenatal MRI confirmed these findings prompting a diagnosis of NLS. After birth, radiological imaging did not detect any gross bone abnormalities. DNA was extracted from fetal and parental peripheral blood, all coding exons of PHGDH were PCR-amplified and subjected to Sanger sequencing. Sequencing of PHGDH identified a homozygous premature stop codon mutation (c.1297C>T; p.Gln433*) in fetal DNA, both parents (first-cousins) being heterozygotes. Based on previous associations of mutations in this gene with a milder NLS phenotype, as well as cases of serine deficiency, these observations lend further support to a genotype-phenotype correlation between the degree of PHGDH inactivation and disease severity.