Table 1.

Software packages used in this protocol

Step(s)	Name	Description
1: #10–19	Zeiss ZEN	Proprietary Windows software included with Zeiss Lightsheet Z.1 and 7 microscopes – necessary for setup and acquisition on these instruments. For non-Zeiss imaging, refer to the software included with your microscope.
1: #14, #24–25	ZLAPS (ZEN lightsheet adaptive positioning system)	Open-source IT3 and ImageJ scripted utility (Windows) that interfaces with ZEN to provide adaptive time-lapse acquisitions. Such features may be included in future versions of ZEN or your microscope’s software, obviating this package.
2–6: #28–71	(k)ubuntu 18.04 to 24.04	Open-source Linux operating system for PC x86–64 hardware that we use for our computational workflow. User can adapt virtually all software below for Windows, although pre-built F-TGMM is only provided for Linux (user would need to download F-TGMM sources as well as nVidia CUDA toolkit and compile for Windows if desired).
2: #28–71	Fiji6 “Fiji is just ImageJ”	Open-source multi-platform Java-based application evolved from the original NIH ImageJ, with many plugins and tools included. Needed for virtually all computation steps of this workflow.
2: #28–30; 3: #44, #47; 4: #53	LSFM Processing Scripts1	Collection of macros in Fiji for automating deconvolution, filtering, and format interconversion. Additionally contains Perl and Python scripts to augment BigStitcher and TGMM integration.
2: #28–29	PSF Generator & Parallel Spectral Deconvolution	Fiji plugins that are needed for single-view deconvolution performed by LSFM Processing Scripts.
3: #31–47	BigStitcher1,7	Fiji plugin for registering (aligning) all image stacks together in 4d, and fusing them into volumes (free from motion artifact, drift, and jitter) for each time point and channel. We recommend using our multiview-reconstruction.jar (see key resources table for link to github repository) containing performance enhancements, additional user options, and Lightweight Content Based Fusion.
3–4: #47–55	KLB Fiji integration1,2 & KLB library2	System library, Python package, and Fiji utility for working with klb file format. klb is not absolutely necessary although it is highly recommended for fused datasets as the preferred format for (F-)TGMM input images.
4: #50–55	F-TGMM v2.51	Application in C++ and nVidia CUDA, compatible only with nVidia (GP)GPUs, for segmentation and tracking of fused 4d datasets (klb or tif formatted). Pre-built only for Linux, though can be built for Windows also.
3: #49; 4: #50–57; 5: #59–61	TrackingFiles	Collection of scripts, lookup tables, and configuration files for use with F-TGMM, SVF, and MaMuT.
5: #59–64	SVF1,2	Python application for statistically resolving TGMM tracking solutions into vector-like morphogenetic maps. Improves spatial accuracy and reconstructs continuous cell tracks across the full duration of the dataset. Affords backward and forward propagation of cell or tissue labels (identities) that are annotated by the user.
4: #58; 5: #64; 6: #69–70	MaMuT1,8	Fiji plugin for visualizing tracking solutions, either directly from TGMM (raw) or outputted by SVF. We recommend using our MaMuT.jar (see key resources table for link to github repository) containing additional user options and 3d viewer.
5: #55–56	MaMuT script library1	Perl scripts for manipulating MaMuT datasets via filtering, coloring, annotating, subsetting, merging, and exporting track data to visualize or quantify cell behaviors.
2–6: #28–71	LSFMProcessing-Kubuntu	USB Bootable custom live Linux distribution (Kubuntu 24.04 base) containing proprietary nVidia driers (version 550), and all software above other than ZEN and ZLAPS (see key resources table for link to GitLab repository for instructions to download and use).