Multifocal imaging for precise, label-free tracking of fast biological processes in 3D

Abstract

Many biological processes happen on a nano-to millimeter scale and within milliseconds.Established methods such as confocal microscopy are suitable for precise 3D recordings but lack the temporal or spatial resolution to resolve fast 3D processes and require labeled samples.Multifocal imaging (MFI) allows high-speed 3D imaging but suffers from the compromise between spatial resolution and field-of-view (FOV), requiring bright fluorescent labels and limiting its application. Here, we present a new approach for high-resolution, label-free, highspeed MFI, based on dark-field microscopy and operative over large volumes. We introduce a 3D reconstruction algorithm that increases resolution and depth of the sampled volume without compromising speed and FOV. This allowed us to characterize the flagellar beat of human sperm and surrounding fluid flow with a precision below the Abbe limit, in a large volume, and at high speed. Our MFI concept is cost-effective, can be easily built, and does not rely on object labeling, making it broadly applicable.

2D and 3D Human Induced Pluripotent Stem Cell-Based Models to Dissect Primary Cilium Involvement during Neocortical Development

“…Immunostaining analyses using antibodies raised against PC markers show that those NSPCs harbor PC (Figure 2A). By combining two complementary open-source tools, Ilastik, a machine-learning-based image analysis tool useful for PC segmentation 28 and CiliaQ, a Fiji/ImageJ plugin package 29 , that enable 3D reconstruction of PC from 3D confocal image stacks, several structural parameters can be easily evaluated including the number of PC and their length. PC function of NSPCs can also be evaluated by testing the transduction of the Hedgehog signaling pathway.…”

CiliaQ: a simple, open-source software for automated quantification of ciliary morphology and fluorescence in 2D, 3D, and 4D images

“…Other tools and approaches to automatically detect and reconstruct cilia of tissue cells in digital images have been described, starting in the eighties [31][32][33]55]. However, these studies are either limited to the characterization of individual parameters, e.g., only the ciliary centerline in 2D [55], the ciliary length in 3D [32,33], the length and frequency (% of ciliated cells) in 2D [31], the ciliary orientation and distribution [28], or to characterize individual cilia per image [24,56]. Notably, the method by Ferreira et al [28] requires manual optimization of the imaging setup and relies on inferring ciliary orientation from imaging artifacts, and thus does not Fig.…”

CiliaQ – a simple, open-source software for automated quantification of ciliary morphology and fluorescence in 2D, 3D, and 4D images

“…However, these studies are either limited to the characterization of individual parameters, e.g. only the ciliary centerline in 2D [40], the ciliary length in 3D [17,18], the length and frequency (% of ciliated cells) in 2D [16], or to characterizing individual cilia per image [41,42]. In contrast, CiliaQ provides a comprehensive list of different parameters for the morphology, orientation, bending, and protein content of many cilia in 2D images, 3D images, and time-lapse 2D or 3D images.…”

“…However, these studies are either limited to the characterization of individual parameters, e.g. only the ciliary centerline in 2D [55], the ciliary length in 3D [32,33], the length and frequency (% of ciliated cells) in 2D [31], the ciliary orientation and distribution [56], or to characterize individual cilia per image [24,57]. Notably, the method by Ferreira et al [56] requires manual optimization of the imaging setup and relies on inferring ciliary orientation from imaging artefacts, thus does not allow a trivial, precise reconstruction of ciliary morphology parameters like the ciliary length or bending.…”