Modulation of nitrogen vacancy charge state and fluorescence in nanodiamonds using electrochemical potential

The negatively charged nitrogen vacancy (NV⁻) center in diamond has attracted strong interest for a wide range of sensing and quantum information processing applications. To this end, recent work has focused on controlling the NV charge state, whose stability strongly depends on its electrostatic environment. Here, we demonstrate that the charge state and fluorescence dynamics of single NV centers in nanodiamonds with different surface terminations can be controlled by an externally applied potential difference in an electrochemical cell. The voltage dependence of the NV charge state can be used to stabilize the NV⁻ state for spin-based sensing protocols and provides a method of charge state-dependent fluorescence sensing of electrochemical potentials. We detect clear NV fluorescence modulation for voltage changes down to 100 mV, with a single NV and down to 20 mV with multiple NV centers in a wide-field imaging mode. These results suggest that NV centers in nanodiamonds could enable parallel optical detection of biologically relevant electrochemical potentials.United States. Army Research Office (W911NF-12-1-0594)United States. National Institutes of Health (1R01NS087950)United States. Defense Advanced Research Projects Agency (D14PC00121)United States. Defense Advanced Research Projects Agency (HR0011-14-C-0018)United States. National Institutes of Health (1R43MH102942-01)National Science Foundation (U.S.) (1122374

Ocean warming is the key filter for successful colonization of the migrant octocoral Melithaea erythraea (Ehrenberg, 1834) in the Eastern Mediterranean Sea

Climate, which sets broad limits for migrating species, is considered a key filter to species migration between contrasting marine environments. The Southeast Mediterranean Sea (SEMS) is one of the regions where ocean temperatures are rising the fastest under recent climate change. Also, it is the most vulnerable marine region to species introductions. Here, we explore the factors which enabled the colonization of the endemic Red Sea octocoral Melithaea erythraea (Ehrenberg, 1834) along the SEMS coast, using sclerite oxygen and carbon stable isotope composition (delta O-18(SC) and delta C-13(SC)), morphology, and crystallography. The unique conditions presented by the SEMS include a greater temperature range (similar to 15 degrees C) and ultra-oligotrophy, and these are reflected by the lower delta C-13(SC) values. This is indicative of a larger metabolic carbon intake during calcification, as well as an increase in crystal size, a decrease of octocoral wart density and thickness of the migrating octocoral sclerites compared to the Red Sea samples. This suggests increased stress conditions, affecting sclerite deposition of the SEMS migrating octocoral. The delta(OSC)-O-18 range of the migrating M. erythraea indicates a preference for warm water sclerite deposition, similar to the native depositional temperature range of 21-28 degrees C. These findings are associated with the observed increase of minimum temperatures in winter for this region, at a rate of 0.35 +/- 0.27 degrees C decade(-1) over the last 30 years, and thus the region is becoming more hospitable to the IndoPacific M. erythraea. This study shows a clear case study of "tropicalization" of the Mediterranean Sea due to recent warming

(Invited) Neuroengineering Overview

Neuroengineering, the field of creating new tools for neuroscience, is an emerging field which includes the invention of technologies to activate and silence electrical activity in the brain, technologies to read out electrical activity from cells in the brain, and tools for mapping the brain. In this talk I will give an overview of technologies that recently emerged from our lab. First, recent optogenetic tools have begun to reach the physical limits of performance. The optogenetic molecules (opsins) Chronos and Chrimson enable activation of distinct neural populations with multiple colors of light, and Jaws is a red shifted inhibitor of neural activity that enables noninvasive neural silencing. Since opsins express all along cell membranes, light focused on one cell body will result in artifactual activation of multiple cells, whose processes are physically touching the neuron of interest. To tackle this problem, we designed a class of somatic opsins that express mainly at the cell body. This, in combination with holographic stimulation enables single cell optogenetics at millisecond temporal resolution. Activity sensors represent an area of great interest in neuroengineering. The voltage sensor Archon is an archaerhodopsin-based molecule with a high voltage sensitivity and brightness compared to its predecessors. The aforementioned cross talk problem also pertains to sensors: since cell-processes are touching cell bodies, the signal coming from a cell body could in principle originate from nearby cells. To solve this problem we developed a GCaMP6f molecule that is retained in the cell body only, called somaGCaMP6f. This molecule enables low crosstalk physiological imaging in mice and fish. Lastly, we have developed a technology that greatly facilitates the mapping of the brain. Optical super-resolution techniques are slow and costly, and accordingly do not scale well to large-scale brain circuitry. Instead of improving the resolving power of the microscope, we have found a way to physically expand biological specimens 4x-20x in linear dimension, in an isotropic fashion. This method, which we call expansion microscopy (ExM), enables the mapping of molecules of interest across cells and tissues of extended scale, and thus facilitates the analysis of neural circuits across scales of relevance to understanding brain function. </jats:p

Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.</jats:p

Machine learning analysis of naïve B-cell receptor repertoires stratifies celiac disease patients and controls

AbstractCeliac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.</jats:p

Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers