Phi Optics, Inc.  

Patented Spatial Light Interference Microscopy (SLIM) is implemented as an add-on to the camera port of your research microscope (Zeiss, Nikon, Leica, Evident) for any magnification and immersion (10X-100X, dry or water/oil and more). SLIM collects live, label-free and quantitative information (morphology, dry mass, RI) with subcellular resolution from milliseconds to days. Phi Optics CellVista SLIM modules provide nanometer and femtogram sensitivity to morphology and mass, high SNR with no speckles background, optical sectioning for 3D tomography. Drug discovery, protein particles, proliferation, viability, cytotoxicity, immune cell killing, and more population assays can be done with sub-cellular resolution. Use regular sample holders (single or multi-wells) or microfluidics with minimal or no sample preparation. Correlative imaging (SLIM and fluorescence) can be acquired with same large field of view, high QE camera for pixel level multi-channel registration to input into latest machine learning models.

Patented Gradient Light Interference Microscopy (GLIM) is implemented as an add-on to the camera port of your research microscope (Zeiss, Nikon, Leica, Evident) for any magnification and immersion (10X-100X, dry or water/oil and more). GLIM collects live, label-free and quantitative information (morphology, dry mass, RI) with subcellular resolution from milliseconds to days. Phi Optics CellVista GLIM modules provide high SNR with no speckles background, optical sectioning for 3D tomography in optically thick specimens: organoids, embryos, small model animals, plant tissue and more. Drug discovery, proliferation, viability, cytotoxicity, immune cell killing, and more assays can be done with sub-cellular resolution. Use regular sample holders (single or multi-wells) or microfluidics with minimal or no sample preparation. Correlative imaging (GLIM and fluorescence) can be acquired with same large field of view, high QE camera for pixel level multi-channel registration to input into latest machine learning models.

Patented Phase Imaging with Computational Specificity (PICS) uses a neural network to learn from colocalized label-free (CellVista SLIM or CellVista GLIM) and ground-truth fluorescence images and predict where specific fluorophores would bind in an unlabeled specimen – thus providing digital staining (label-free) of the QPI quantitative image. The digital staining masks are used to segment QPI (SLIM or GLIM) images to extract dry mass, morphology and refractive index values for 2D and 3D quantitative assays. Even more, the neural network can extract cell cycle-, cell viability-dependent (and more) features from the nanoscale structure measured in QPI images. 2D and 3D assays can be performed with the accuracy and specificity of regular fluorescence, but without the inconveniences associated with phototoxicity and photobleaching.