daime: Digital Image Analysis in Microbial Ecology
daime is a scientific image analysis and visualization software designed for microbiology and microbial ecology. It offers a comprehensive suite of tools for analyzing 2D and 3D microscopy datasets of microorganisms labeled by fluorescence in situ hybridization (FISH) or other fluorescence staining techniques. daime is capable of analyzing single cells and cell aggregates across various sample types, including biofilms, flocs, animal and plant tissues, and microbial enrichment cultures and isolates.
daime has been employed in hundreds of studies across microbial ecology, medical microbiology, microbiome research, and environmental engineering. Its 3D visualization capabilities were used to render the cover illustrations of "Brock - Biology of Microorganisms" (12th edition) and of PNAS vol. 103(7).
All functions of daime are fully integrated and accessible through a user-friendly graphical user interface. Using daime does not require any programming skills.
daime Reference
Daims H, Lücker S, Wagner M. 2006. daime, a novel image analysis program for microbial ecology and biofilm research. Environ. Microbiol. 8: 200-213. PubMed
daime is an acronym for "digital image analysis in microbial ecology". The software is developed and coded by Holger Daims. Any similarity of the acronym with his surname is pure coincidence ;-)
daime is free for use in academic research and education.
How to get daime
daime is currently available for Windows and Linux operating systems. Macintosh users may run daime for Windows or Linux in a virtual machine (please note that this works only for Mac computers with an Intel microprocessor).
Selected Program features
- daime handles single 2D images, batches of images, and 3D z-stacks
- 2D and 3D image segmentation
- Quantification of microbial abundance
- Quantification of spatial arrangement patterns
- Virtual slicing of stratified biofilms
- Melting curve analysis of rRNA-targeted FISH probes
- Interactive 2D and 3D visualization
- Connection and data exchange with R
Analysis of single 2D images, batches of images, and 3D z-stacks
Working with image batches in daime significantly streamlines the evaluation of large image datasets. Users can analyze hundreds of images with just a few mouse clicks, and the program automatically summarizes the results.
daime is designed to work with both single images and batches of multiple images for collective analysis, such as replicate images needed for quantitative assessments. It also supports the import and analysis of confocal image stacks (z-stacks), which contain 3D image data. Most features of the software are tailored for both 2D and 3D image processing and analysis.
2D and 3D Image segmentation
daime includes a powerful object editor that is fully integrated into the 2D and 3D visualization modules. Users can interactively select objects for various analyses that daime can perform. For instance, the 3D algorithms enable users to virtually "walk through" a 3D z-stack and select 3D objects by point-and-click using the mouse.
Image segmentation, which includes object recognition such as microbial cells and cell aggregates, is a critical yet challenging aspect of image analysis. daime is capable of detecting 2D objects in single images and batches, as well as 3D objects in z-stacks. It offers features for fully automatic, semi-automatic, and manual image segmentation. Objects are recognized based on criteria like fluorescence intensity, edges, and color.
daime allows for the measurement of various features of 2D and 3D objects, including object size, brightness, surface area, volume, and other parameters. Segmented images are also utilized for quantifying microbial abundance, analyzing spatial arrangement patterns, and other image analysis functionalities.
Quantification of Microbial abundance
daime counts objects, such as microbial cells and cell clusters, in both 2D and 3D images. Additionally, it can quantify the biovolume fraction of a population relative to the total biovolume of all microbes using batches of 2D-segmented images. This involves comparing one batch showing a specific FISH probe signal with another batch showing the signal from a general bacterial probe mix or an appropriate nucleic acid stain. Notably, this stereological approach estimates a 3D parameter (biovolume fraction) from 2D images, avoiding the time-consuming process of acquiring z-stacks. Biovolume fractions are often more informative than cell counts because biovolume represents the "biochemical reaction space" of a microbial population, highlighting that a few large cells may have the same biovolume as many smaller cells.
Quantification of spatial arrangement patterns
Spatial arrangement patterns in microbial communities can be complex and subtle, making visual observation under a microscope both difficult and unreliable for accurate evaluation. daime addresses this challenge with a suite of unique stereological algorithms designed to quantify the spatial arrangement of microbial populations labeled with specific FISH probes or other fluorescent markers. These algorithms are applicable to both batches of 2D-segmented images and 3D-segmented z-stacks.
The spatial arrangement of microbes, including co-aggregation or avoidance, is a crucial aspect of microbial communities in biofilms and other spatially structured samples. This arrangement can indicate significant biological interactions between microorganisms, such as mutualistic symbioses, competition for resources, and predator-prey relationships.
Virtual slicing of stratified biofilms
Stratification within biofilms, flocs, or granules often mirrors the ecophysiological properties of microorganisms in varying depth zones. Not only abundance but also spatial arrangement patterns and other characteristics of microbial populations may vary across the layers of a stratified biofilm. daime facilitates the quantification of stratification-related phenomena by allowing for the virtual slicing of biofilm images. Once sliced, all image analysis functions of the software can be used to assess the microbial populations in each depth zone.
The automatic image slicing algorithm is highly flexible. Users can adjust the slicing direction, thickness of sections, and other parameters. Up to four slicing directions can be combined, enabling the analysis of even spherical or tube-shaped structures.
Melting curve analysis of FISH probes
When designing a new rRNA-targeted FISH probe, it is crucial to experimentally determine the optimal stringency conditions for specific hybridization. This typically involves conducting a series of FISH experiments using target and non-target organisms with increasing formamide (FA) concentrations in the hybridization buffers. daime provides a specialized feature for evaluating such FA series. It measures the fluorescence intensities of cells in a batch of images taken after FISH at varying FA concentrations. From these measurements, the probe dissociation profile (melting curve) is then calculated based on the mean fluorescence intensities.
Interactive 2D and 3D visualization
The capabilities of confocal microscopy are fully realized when z-stacks are projected in 3D for interactive exploration. Have you ever dived into your microbial samples? daime offers high-speed 3D visualization tools that render z-stacks in real-time, allowing for free rotation and virtual "fly-through". Multiple z-stacks can be integrated into the same 3D scene, and rendering parameters are fully customizable to highlight critical features of 3D datasets. Hidden structures can be revealed through semi-transparent surface rendering or clipping, and the 3D effect is enhanced by virtual lighting and shadows. daime also supports stereo anaglyphs for red-green glasses at interactive frame rates, and popular display modes like maximum intensity projection.
Beyond producing still images, daime simplifies movie creation: define a few keyframes, and daime will compute the entire animation sequence. Rendered images and animations can be exported as image or movie files, ideal for use in presentations and publications.
Connection and data exchange with R
The R language and environment for statistical computing and graphics is widely used for various types of data analyses. While daime functions as a stand-alone software, it can be optionally linked with an existing R installation to enhance data plotting and analysis capabilities. This connection is seamless, allowing users to harness the combined power of daime and R. No R programming skills are necessary to benefit from this integration; however, users who are proficient in R can take advantage of additional features. daime can generate R scripts to reproduce and fine-tune result plots directly in R. Additionally, it exports all measured data in tabular formats, facilitating easy import into R for customized analyses.
