Harvard College

Massachusetts Institute of Technology

Jeff Lichtman

Narayanan Kasthuri

Richard Schalek

Kenneth Jeffrey Hayworth

Juan Carlos Tapia

Nir N Shavit

Lu Mi

Aravinathan Samuel

David J Mooney

Ronald Lee Walsworth

Herman H Vandenburgh

David Glenn

Cristina Borselli

Dimitry Shvartsman

Hannah Storrie

Huiliang Zhang

A system and method is provided for rapidly collecting high quality images of a specimen through controlling a re-focusable beam of an electron microscope. An intelligent acquisition system instructs the electron microscope to perform an initial low-resolution scan of a sample. A low-resolution image of the sample is received by the intelligent acquisition system as scanned image information from the electron microscope. The intelligent acquisition system then determines regions of interest within the low-resolution image and instructs the electron microscope to perform a high-resolution scan of the sample, only in areas of the sample corresponding to the determined regions of interest or portions of the determined regions of interest, so that other regions within the sample are not scanned at high-resolution, where the high-resolution scanning in the regions of interest is guided by a probability map using a deep neural network for segmentation.

System and method for learning-guided electron microscopy

A system and method is provided for rapidly collecting high quality images of a specimen through controlling a re-focusable beam of an electron microscope. An intelligent acquisition system instructs the electron microscope to perform an initial low-resolution scan of a sample. A low-resolution image of the sample is received by the intelligent acquisition system as scanned image information from the electron microscope. The intelligent acquisition system then determines regions of interest within the low-resolution image and instructs the electron microscope to perform a high-resolution scan of the sample, only in areas of the sample corresponding to the determined regions of interest or portions of the determined regions of interest, so that other regions within the sample are not scanned at high-resolution. The intelligent acquisition system then reconstructs an image using the collected high-resolution scan pixels and pixels in the received low-resolution image.

Methods, apparatuses and systems for facilitating automated or semi-automated collection of tissue samples cut by a microtome. In one example, a collection apparatus may be moved back and forth between respective positions at which the collection apparatus is operatively coupled to a microtome so as to collect cut tissue samples, or routine access to the microtome is provided. Relatively easy movement and positioning of the collection apparatus is facilitated, while at the same time ensuring structural stability and appropriate alignment and/or isolation between the collection apparatus and the microtome. A fluid reservoir receives samples cut by the microtome, and the collection apparatus may collect samples via a conveyor-like substrate disposed near/in the reservoir. A linear movement of the substrate may be controlled based on a cutting rate of the microtome, and the fluid level in the reservoir may be automatically maintained to facilitate effective sample collection.

Methods, apparatuses and systems for collection of tissue sections

An improved device and method for extended repair and regeneration of muscle tissue. An exemplary device comprises (a) a scaffold comprising an ECM component; (b) a combination of growth factors such as VEGF and IGF; and (c) a population of myogenic cells. Implantation of the device leads to muscle regeneration and repair over an extended period of time.

Enhancement of skeletal muscle stem cell engraftment by dual delivery of VEGF and IGF-1

Multi-color CL images of nanoparticle samples may be generated, by irradiating with a scanning electron beam a nanoparticle sample that containing a plurality of spectrally distinct optical emitters configured to generate CL light at respective different color channels, then detecting the CL light from the nanoparticles to generate multi-color NP-CL images of the nanoparticle sample. In some embodiments, SE (secondary electron) images of the sample may be acquire, substantially simultaneously with the acquisition of the CL images, so as to generate correlative NP-CL and SE images of the nanoparticle sample. In some embodiments, the nanoparticles may be surface-functionalized so that the nanoparticles selectively bind only to particular structures of interest.

Multi-color nanoscale imaging based on nanoparticle cathodoluminescence

Growing community of inventors

Cambridge, MA, United States of America

Jeff Lichtman