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Nanometric probes based on surface-enhanced Raman scattering (SERS) are promising candidates for all-optical environmental, biological and technological sensing applications with intrinsic quantitative molecular specificity. However, the effectiveness of SERS probes depends on a delicate trade-off between particle size, stability and brightness that has so far hindered their wide application in SERS imaging methodologies. In this Article, we introduce holographic Raman microscopy, which allows single-shot three-dimensional single-particle localization. We validate our approach by simultaneously performing Fourier transform Raman spectroscopy of individual SERS nanoparticles and Raman holography, using shearing interferometry to extract both the phase and the amplitude of wide-field Raman images and ultimately localize and track single SERS nanoparticles inside living cells in three dimensions. Our results represent a step towards multiplexed single-shot three-dimensional concentration mapping in many different scenarios, including live cell and tissue interrogation and complex anti-counterfeiting applications.Holography of incoherent emission from SERS probes allows multiplexed single-particle localization in three dimensions in one shot using a wide-field microscope.

Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and ‘straightened’ freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90–100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze. Microscopes have been used to decrypt the tiny details of life since the 17th century. Now, the advent of 3D microscopy allows scientists to build up detailed pictures of living cells and tissues. In that effort, automation is becoming increasingly important so that scientists can analyze the resulting images and understand how bodies grow, heal and respond to changes such as drug therapies. In particular, algorithms can help to spot cells in the picture (called cell segmentation), and then to follow these cells over time across multiple images (known as cell tracking). However, performing these analyses on 3D images over a given period has been quite challenging. In addition, the algorithms that have already been created are often not user-friendly, and they can only be applied to a specific dataset gathered through a particular scientific method. As a response, Wen et al. developed a new program called 3DeeCellTracker, which runs on a desktop computer and uses a type of artificial intelligence known as deep learning to produce consistent results. Crucially, 3DeeCellTracker can be used to analyze various types of images taken using different types of cutting-edge microscope systems. And indeed, the algorithm was then harnessed to track the activity of nerve cells in moving microscopic worms, of beating heart cells in a young small fish, and of cancer cells grown in the lab. This versatile tool can now be used across biology, medical research and drug development to help monitor cell activities.

Much is still not understood about how gene regulatory interactions control cell fate decisions in single cells, in part due to the difficulty of directly observing gene regulatory processes in vivo. We introduce here a novel integrated setup consisting of a microfluidic chip and accompanying analysis software that enable long-term quantitative tracking of growth and gene expression in single cells. The dual-input Mother Machine (DIMM) chip enables controlled and continuous variation of external conditions, allowing direct observation of gene regulatory responses to changing conditions in single cells. The Mother Machine Analyzer (MoMA) software achieves unprecedented accuracy in segmenting and tracking cells, and streamlines high-throughput curation with a novel leveraged editing procedure. We demonstrate the power of the method by uncovering several novel features of an iconic gene regulatory program: the induction of Escherichia coli ’s lac operon in response to a switch from glucose to lactose. How gene regulatory pathways control cell fate decisions in single cells is not fully understood. Here the authors present an integrated dual-input microfluidic chip and a linked analysis software, enabling tracking of gene regulatory responses of single bacterial cells to changing conditions.

Single-molecule localization microscopy (SMLM) is a rapidly evolving technique to resolve subcellular structures and single-molecule dynamics at the nanoscale. Here, we employ conventional BODIPY conjugates for live-cell SMLM via their previously reported red-shifted ground-state dimers (D II ), which transiently form through bi-molecular encounters and emit bright single-molecule fluorescence. We employ the versatility of D II -state SMLM to resolve the nanoscopic spatial regulation and dynamics of single fatty acid analogs (FAas) and lipid droplets (LDs) in living yeast and mammalian cells with two colors. In fed cells, FAas localize to the endoplasmic reticulum and LDs of ~125 nm diameter. Upon fasting, however, FAas form dense, non-LD clusters of ~100 nm diameter at the plasma membrane and transition from free diffusion to confined immobilization. Our reported SMLM capability of conventional BODIPY conjugates is further demonstrated by imaging lysosomes in mammalian cells and enables simple and versatile live-cell imaging of sub-cellular structures at the nanoscale. Single-molecule localization microscopy (SMLM) requires the use of fluorophores with specific sets of properties. Here the authors employ conventional BODIPY dyes as SMLM fluorophores by making use of rarely reported red-shifted ground state BODIPY dimers to image fatty acids, lipid droplets and lysosomes at single-molecule resolution.

NOTCH signaling is required for the arterial specification and formation of hematopoietic stem cells (HSCs) and lympho-myeloid progenitors in the embryonic aorta-gonad-mesonephros region and extraembryonic vasculature from a distinct lineage of vascular endothelial cells with hemogenic potential. However, the role of NOTCH signaling in hemogenic endothelium (HE) specification from human pluripotent stem cell (hPSC) has not been studied. Here, using a chemically defined hPSC differentiation system combined with the use of DLL1-Fc and DAPT to manipulate NOTCH, we discover that NOTCH activation in hPSC-derived immature HE progenitors leads to formation of CD144 + CD43 − CD73 − DLL4 + Runx1 + 23-GFP + arterial-type HE, which requires NOTCH signaling to undergo endothelial-to-hematopoietic transition and produce definitive lympho-myeloid and erythroid cells. These findings demonstrate that NOTCH-mediated arterialization of HE is an essential prerequisite for establishing definitive lympho-myeloid program and suggest that exploring molecular pathways that lead to arterial specification may aid in vitro approaches to enhance definitive hematopoiesis from hPSCs. It is unclear whether arterial specification is required for hematopoietic stem cell formation. Here, the authors use a chemically defined human pluripotent stem cell (hPSC) differentiation system to show the role of NOTCH signaling in forming arterial-type hemogenic endothelial cells.

Convolutional neural networks (ConvNets) have proven to be successful in both the classification and semantic segmentation of cell images. Here we establish a method for cell type classification utilizing images taken with a benchtop microscope directly from cell culture flasks, eliminating the need for a dedicated imaging platform. Significant flask-to-flask morphological heterogeneity was discovered and overcome to support network generalization to novel data. Cell density was found to be a prominent source of heterogeneity even when cells are not in contact. For the same cell types, expert classification was poor for single-cell images and better for multi-cell images, suggesting experts rely on the identification of characteristic phenotypes within subsets of each population. We also introduce Self-Label Clustering (SLC), an unsupervised clustering method relying on feature extraction from the hidden layers of a ConvNet, capable of cellular morphological phenotyping. This clustering approach is able to identify distinct morphological phenotypes within a cell type, some of which are observed to be cell density dependent. Finally, our cell classification algorithm was able to accurately identify cells in mixed populations, showing that ConvNet cell type classification can be a label-free alternative to traditional cell sorting and identification.

The movement of molecules inside living cells is a fundamental feature of biological processes. The ability to both observe and analyse the details of molecular diffusion in vivo at the single-molecule and single-cell level can add significant insight into understanding molecular architectures of diffusing molecules and the nanoscale environment in which the molecules diffuse. The tool of choice for monitoring dynamic molecular localization in live cells is fluorescence microscopy, especially so combining total internal reflection fluorescence with the use of fluorescent protein (FP) reporters in offering exceptional imaging contrast for dynamic processes in the cell membrane under relatively physiological conditions compared with competing single-molecule techniques. There exist several different complex modes of diffusion, and discriminating these from each other is challenging at the molecular level owing to underlying stochastic behaviour. Analysis is traditionally performed using mean square displacements of tracked particles; however, this generally requires more data points than is typical for single FP tracks owing to photophysical instability. Presented here is a novel approach allowing robust Bayesian ranking of diffusion processes to discriminate multiple complex modes probabilistically. It is a computational approach that biologists can use to understand single-molecule features in live cells.

Flow cytometers measure fluorescence and light scattering and analyze multiple physical characteristics of a large population of single cells as cells flow in a fluid stream through an excitation light beam. Although flow cytometers have massive statistical power due to their single cell resolution and high throughput, they produce no information about cell morphology or spatial resolution offered by microscopy, which is a much wanted feature missing in almost all flow cytometers. In this paper, we invent a method of spatial-temporal transformation to provide flow cytometers with cell imaging capabilities. The method uses mathematical algorithms and a spatial filter as the only hardware needed to give flow cytometers imaging capabilities. Instead of CCDs or any megapixel cameras found in any imaging systems, we obtain high quality image of fast moving cells in a flow cytometer using PMT detectors, thus obtaining high throughput in manners fully compatible with existing cytometers. To prove the concept, we demonstrate cell imaging for cells travelling at a velocity of 0.2 m/s in a microfluidic channel, corresponding to a throughput of approximately 1,000 cells per second.

Anemia and iron deficiency continue to be the most prevalent nutritional disorders in the world, affecting billions of people in both developed and developing countries. The initial diagnosis of anemia is typically based on several markers, including red blood cell (RBC) count, hematocrit and total hemoglobin. Using modern hematology analyzers, erythrocyte parameters such as mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), etc. are also being used. However, most of these commercially available analyzers pose several disadvantages: they are expensive instruments that require significant bench space and are heavy enough to limit their use to a specific lab and lead to a delay in results, making them less practical as a point-of-care instrument that can be used for swift clinical evaluation. Thus, there is a need for a portable and economical hematology analyzer that can be used at the point of need. In this work, we evaluated the performance of a system referred to as the cell tracking velocimetry (CTV) to measure several hematological parameters from fresh human blood obtained from healthy donors and from sickle cell disease subjects. Our system, based on the paramagnetic behavior that deoxyhemoglobin or methemoglobin containing RBCs experience when suspended in water after applying a magnetic field, uses a combination of magnets and microfluidics and has the ability to track the movement of thousands of red cells in a short period of time. This allows us to measure not only traditional RBC indices but also novel parameters that are only available for analyzers that assess erythrocytes on a cell by cell basis. As such, we report, for the first time, the use of our CTV as a hematology analyzer that is able to measure MCV, MCH, mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), the percentage of hypochromic cells (which is an indicator of insufficient marrow iron supply that reflects recent iron reduction), and the correlation coefficients between these metrics. Our initial results indicate that most of the parameters measured with CTV are within the normal range for healthy adults. Only the parameters related to the red cell volume (primarily MCV and RDW) were outside the normal range. We observed significant discrepancies between the MCV measured by our technology (and also by an automated cell counter) and the manual method that calculates MCV through the hematocrit obtained by packed cell volume, which are attributed to the artifacts of plasma trapping and cell shrinkage. While there may be limitations for measuring MCV, this device offers a novel point of care instrument to provide rapid RBC parameters such as iron stores that are otherwise not rapidly available to the clinician. Thus, our CTV is a promising technology with the potential to be employed as an accurate, economical, portable and fast hematology analyzer after applying instrument-specific reference ranges or correction factors.

Cancers contain a wide diversity of cell types that are defined by differentiation states, genetic mutations and altered epigenetic programmes that impart functional diversity to individual cells. Elevated tumour cell heterogeneity is linked with progression, therapy resistance and relapse. Yet, imaging of tumour cell heterogeneity and the hallmarks of cancer has been a technical and biological challenge. Here we develop optically clear immune-compromised rag2 E450fs (casper) zebrafish for optimized cell transplantation and direct visualization of fluorescently labelled cancer cells at single-cell resolution. Tumour engraftment permits dynamic imaging of neovascularization, niche partitioning of tumour-propagating cells in embryonal rhabdomyosarcoma, emergence of clonal dominance in T-cell acute lymphoblastic leukaemia and tumour evolution resulting in elevated growth and metastasis in BRAF V600E -driven melanoma. Cell transplantation approaches using optically clear immune-compromised zebrafish provide unique opportunities to uncover biology underlying cancer and to dynamically visualize cancer processes at single-cell resolution in vivo. Direct visualisation of heterogeneous cell populations in live animals has been challenging. Here, the authors optimize cell transplantation into optically clear immune-deficient zebrafish, and use intravital imaging to track and to assess functional diversity of individual cancer cells in vivo .