Extant Life Volumetric Imaging System
(Microscopic Analysis of Cells, Chris Lindensmith – Co-PI)
ELVIS is a multi-microscope system that enables a true “needle-in-a-haystack” search of a large sample volume at high resolution. It includes fluorescent labeling of potential cells to associate biochemical markers with specific objects.
The ELVIS microscope suite combines high throughput with high resolution and fluorescence to identify cell structure and motility in a large sample volume.
Digital Holographic Microscope (DHM)
The DHM observes objects at many scales simultaneously without adjustment, provides better than 0.8-micron spatial resolution across a 0.25 mm³ flow-through cell, and has the ability to detect fewer than 100 cell-like particles in a 1 mL volume. It enables index of refraction imaging to help identify and discriminate minerals from possible cells. Machine-learning algorithms are used to extract particle tracks and identify lifelike movement of single-cell organisms. Its time series imaging enables tracking of particles to determine their density and whether they may have their own mechanisms of motion separate from the background flow.
Lightfield Volume Fluorescent Imager (VFI)
The VFI images the same volume as the DHM simultaneously, but at lower resolution, to spatially correlate the fluorescent detection of proteins, cell walls, and nucleic acids that may be present for further identification of potential biological material. The VFI uses machine-learning algorithms for prioritizing high signal-to-noise fluorescence data.
The DHM and VFI microscopes record simultaneous movies of the same 40 µL volume with 0.8 µm resolution (DHM, left) and 4 µm resolution (VFI, right). The DHM shows the cell’s shape with the high resolution, while the VFI provides information on chemical content utilizing fluorescence.
High-Resolution Fluorescent Imager (HRFI)
This imager provides 0.2- µm spatial resolution across a 100 µm field of view to observe cell structure based on where fluorescent tags are located.
Machine-learning algorithms are again used to prioritize high signal-to-noise fluorescence data, as well as data with structure.
The HRFI’s 0.2 µm resolution shows the internal structure of larger cells (left), highlighted by fluorescent labels, and chains of smaller cells that have been dyed with a fluorescent label (right).