Alec Soronow is a dedicated professional with a passion for the intersection of technology and biology. He earned his B.S. in Biomoelcular Engineering and Bioinformatics from the University of California, Santa Cruz, where he graduated with honors. During his undergraduate studies, Alec excelled in his coursework, showcasing his strong foundation in both engineering and bioinformatics.
As a freelance developer, Alec has been involved in various projects, including the development of web applications and iOS applications. His versatility and expertise have allowed him to contribute to different aspects of software development, delivering high-quality solutions to his clients.
Alec's research interests lie in the field of machine learning and its applications to biology. He has a keen curiosity about leveraging advanced computational techniques to gain insights into biological systems. Currently, he is pursuing his graduate studies at the University of California, Santa Cruz, as a member of the Euiseok Kim lab. His research focuses on neural anatomy and connectomics in the mouse visual cortex, delving into the intricate structure and connections within this area of the brain.
With his strong academic background, professional experience, and research pursuits, Alec Soronow is making significant contributions to the fields of bioinformatics, neuroscience, and machine learning. His dedication to bridging the gap between technology and biology showcases his commitment to advancing scientific understanding and creating innovative solutions for the future.
To investigate the anatomical organization of neural circuits across the whole brain, it is essential to accurately register the experimental brain tissues to a reference atlas. This procedure is also a prerequisite to quantify the locations and numbers of cells of interest in specific regions. However, it remains challenging to do registration on experimental tissue due to the intrinsic variation among the specimens, tissue deformation introduced by histological processing, and the potential inconsistency of the experimenter during manual annotation. Here, we introduce Bell Jar, a multi-platform analysis tool with semi-automated affine warping of atlas maps onto microscopic images of brain slices, and machine learning-based cell detection. Bell Jar’s GUI and dependency management enable users to obtain accurate results without programming expertise. To compare Bell Jar with previously published methods, we labeled neurons in the mouse visual cortex with either an engineered rabies virus or an adeno-associated virus (AAV) for neural circuit tracing and quantified Bell Jar’s performance at each step of the pipeline for image alignment, segmentation, and cell counting. We demonstrated that Bell Jar’s output is as reliable as manual counting by an expert; it is more accurate than currently available techniques, even with noisy data, and takes less time with fewer user interventions. Bell Jar provides a semi-automated analysis workflow to facilitate the precise mapping of histological images of the mouse brain to the reference atlas, and the quantification of cellular signals users train it to recognize.