Biology subjects

Sabatini, B. L.

Publications and source records attributed to Sabatini, B. L..

2 recordsLinked to original sources

bioRxiv2017

Exploiting modal demultiplexing properties of tapered optical fibers for tailored optogenetic stimulation

Optogenetic control of neural activity in deep brain regions requires precise and flexible light delivery with non-invasive devices. To this end, Tapered Optical Fibers (TFs) represent a minimally-invasive tool that can deliver light over either large brain volumes or spatially confined subregions. This work links the emission properties of TFs with the modal content injected into the fiber, finding that the maximum transversal propagation constant (kt) and the total number of guided modes sustained by the waveguide are key parameters for engineering the mode demultiplexing properties of TFs. Intrinsic features of the optical fiber (numerical aperture and core/cladding diameter) define the optically active segment of the taper (up to [~]3mm), along which a linear relation between the propagating set of kt values and the emission position exists. These site-selective light-delivery properties are preserved at multiple wavelengths, further extending the range of applications expected for tapered fibers for optical control of neural activity.

neuroscience↗

bioRxiv2017

Bots for Software-Assisted Analysis of Image-Based Transcriptomics

We introduce software assistants - bots - for the task of analyzing image-based transcriptomic data. The key steps in this process are detecting nuclei, and counting associated puncta corresponding to labeled RNA. Our main release offers two algorithms for nuclei segmentation, and two for spot detection, to handle data of different complexities. For challenging nuclei segmentation cases, we enable the user to train a stacked Random Forest, which includes novel circularity features that leverage prior knowledge regarding nuclei shape for better instance segmentation. This machine learning model can be trained on a modern CPU-only computer, yet performs comparably with respect to a more hardware-demanding state-of-the-art deep learning approach, as demonstrated through experiments. While the primary motivation for the bots was image-based transcriptomics, we also demonstrate their applicability to the more general problem of scoring \"spots\" in nuclei.

bioinformatics↗