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CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler's infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.

A deep near-infrared spectroscopic survey of 43 photometrically-selected galaxies with redshift z  > 6.5 detects a near-infrared emission line from only a single galaxy; this line is likely to be Lyman α emission at a wavelength of 1.0343 μm, placing this galaxy at z = 7.51. Most distant star-forming galaxy confirmed Hubble Space Telescope data have yielded hundreds of candidates for galaxies with redshifts observed less than one billion years from the Big Bang, but so far distances have been confirmed for only a few of them. Using the newly commissioned MOSFIRE spectrograph on the Keck I telescope, Steven Finkelstein and co-workers have detected a galaxy with an emission line that can be confirmed at a redshift of 7.51, placing it at an epoch 700 million years after the Big Bang. That makes it the most distant spectroscopically confirmed galaxy, This galaxy's colours are consistent with a significant metal content, and it has a surprisingly high star-formation rate of about 330 solar masses per year, more than 100-fold greater than that seen in the Milky Way. The authors suggest that there may be many more such sites of intense star formation in the early Universe than previously expected. Of several dozen galaxies observed spectroscopically that are candidates for having a redshift ( z ) in excess of seven, only five have had their redshifts confirmed via Lyman α emission, at z = 7.008, 7.045, 7.109, 7.213 and 7.215 (refs 1 , 2 , 3 , 4 ). The small fraction of confirmed galaxies may indicate that the neutral fraction in the intergalactic medium rises quickly at z  > 6.5, given that Lyman α is resonantly scattered by neutral gas 3 , 5 , 6 , 7 , 8 . The small samples and limited depth of previous observations, however, makes these conclusions tentative. Here we report a deep near-infrared spectroscopic survey of 43 photometrically-selected galaxies with z  > 6.5. We detect a near-infrared emission line from only a single galaxy, confirming that some process is making Lyman α difficult to detect. The detected emission line at a wavelength of 1.0343 micrometres is likely to be Lyman α emission, placing this galaxy at a redshift z = 7.51, an epoch 700 million years after the Big Bang. This galaxy’s colours are consistent with significant metal content, implying that galaxies become enriched rapidly. We calculate a surprisingly high star-formation rate of about 330 solar masses per year, which is more than a factor of 100 greater than that seen in the Milky Way. Such a galaxy is unexpected in a survey of our size 9 , suggesting that the early Universe may harbour a larger number of intense sites of star formation than expected.

All cellular materials are partitioned between daughters at cell division, but by various mechanisms and with different accuracy. In the yeast Schizosaccharomyces pombe, the mitochondria are pushed to the cell poles by the spindle. We found that mitochondria spatially reequilibrate just before division, and that the mitochondrial volume and DNA-containing nucleoids instead segregate in proportion to the cytoplasm inherited by each daughter. However, nucleoid partitioning errors are suppressed by control at two levels: Mitochondrial volume is actively distributed throughout a cell, and nucleoids are spaced out in semiregular arrays within mitochondria. During the cell cycle, both mitochondria and nucleoids appear to be produced without feedback, creating a net control of fluctuations that is just accurate enough to avoid substantial growth defects.

Mitochondria must grow with the growing cell to ensure proper cellular physiology and inheritance upon division. We measured the physical size of mitochondrial networks in budding yeast and found that mitochondrial network size increased with increasing cell size and that this scaling relation occurred primarily in the bud. The mitochondria-to-cell size ratio continually decreased in aging mothers over successive generations. However, regardless of the mother's age or mitochondrial content, all buds attained the same average ratio. Thus, yeast populations achieve a stable scaling relation between mitochondrial content and cell size despite asymmetry in inheritance.

Mitochondria are dynamic organelles that must precisely control their protein composition according to cellular energy demand. Although nuclear-encoded mRNAs can be localized to the mitochondrial surface, the importance of this localization is unclear. As yeast switch to respiratory metabolism, there is an increase in the fraction of the cytoplasm that is mitochondrial. Our data point to this change in mitochondrial volume fraction increasing the localization of certain nuclear-encoded mRNAs to the surface of the mitochondria. We show that mitochondrial mRNA localization is necessary and sufficient to increase protein production to levels required during respiratory growth. Furthermore, we find that ribosome stalling impacts mRNA sensitivity to mitochondrial volume fraction and counterintuitively leads to enhanced protein synthesis by increasing mRNA localization to mitochondria. This points to a mechanism by which cells are able to use translation elongation and the geometric constraints of the cell to fine-tune organelle-specific gene expression through mRNA localization.

The morphology of mitochondrial networks is complex and highly varied, yet vital to cell function. The first step toward an integrative understanding of how mitochondrial morphology is generated and regulated is to define the interdependent geometrical features and their dynamics that together generate the morphology of a mitochondrial network within a cell. Distinct aspects of the size, shape, position, and dynamics of mitochondrial networks are described and examples of how these features depend on one another discussed.

Crawling cells of various morphologies displace themselves in their biological environments by a similar overall mechanism of protrusion through actin assembly at the front coordinated with retraction at the rear. Different cell types organize very distinct protruding structures, yet they do so through conserved biochemical mechanisms to regulate actin polymerization dynamics and vary the mechanical properties of these structures. The moving cell must spatially and temporally regulate the biochemical interactions of its protein components to exert control over higher-order dynamic structures created by these proteins and global cellular responses four or more orders of magnitude larger in scale and longer in time than the individual protein-protein interactions that comprise them. To fulfill its biological role, a cell globally responds with high sensitivity to a local perturbation or signal and coordinates its many intracellular actin-based functional structures with the physical environment it experiences to produce directed movement. This review attempts to codify some unifying principles for cell motility that span organizational scales from single protein polymer filaments to whole crawling cells.

A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and discuss possible solutions for automating and streamlining the validation process in the next five to ten years.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. This Perspective presents a reliable and comprehensive source of information on pitfalls related to validation metrics in image analysis, with an emphasis on biomedical imaging.

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine 1 , 2 . Here we reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour 3 , 4 —at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the ‘wiring’ of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring. A dataset of 3D images from more than 200,000 human induced pluripotent stem cells is used to develop a framework to analyse cell shape and the location and organization of major intracellular structures.