Tingwei Quan

High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion

Localization-based super-resolution microscopy (or called localization microscopy) rely on repeated imaging and localization of active molecules, and the spatial resolution enhancement of localization microscopy is built upon the sacrifice of its temporal resolution. Developing algorithms for high-density localization of active molecules is a promising approach to increase the speed of localization microscopy. Here we present a new algorithm called SSM_BIC for such purpose. The SSM_BIC combines the advantages of the Structured Sparse Model (SSM) and the Bayesian Information Criterion (BIC). Through simulation and experimental studies, we evaluate systematically the performance between the SSM_BIC and the conventional Sparse algorithm in high-density localization of active molecules. We show that the SSM_BIC is superior in processing single molecule images with weak signal embedded in strong background.

Ultra-fast, high-precision image analysis for localization-based super resolution microscopy

Localization-based super resolution microscopy holds superior performances in live cell imaging, but its widespread use is thus far mainly hindered by the slow image analysis speed. Here we show a powerful image analysis method based on the combination of the maximum likelihood algorithm and a Graphics Processing Unit (GPU). Results indicate that our method is fast enough for real-time processing of experimental images even from fast EMCCD cameras working at full frame rate without compromising localization precision or field of view. This newly developed method is also capable of revealing movements from the images immediately after data acquisition, which is of great benefit to live cell imaging.

Localization capability and limitation of electron-multiplying charge-coupled, scientific complementary metal-oxide semiconductor, and charge-coupled devices for superresolution imaging

Localization of a single fluorescent molecule is required in a number of superresolution imaging techniques for visualizing biological structures at cellular and subcellular levels. The localization capability and limitation of low-light detectors are critical for such a purpose. We present an updated evaluation on the performance of three typical low-light detectors, including a popular electron-multiplying CCD (EMCCD), a newly developed scientific CMOS (sCMOS), and a representative cooled CCD, for superresolution imaging. We find that under some experimental accessible conditions, the sCMOS camera shows a competitive and even better performance than the EMCCD camera, which has long been considered the detector of choice in the field of superresolution imaging.

PALMER: a method capable of parallel localization of multiple emitters for high-density localization microscopy

Developing methods for high-density localization of multiple emitters is a promising approach for enhancing the temporal resolution of localization microscopy while maintaining a desired spatial resolution, but the widespread use of this approach is thus far mainly obstructed by the slow image analysis speed. Here we present a high-density localization method based on the combination of Graphics Processing Unit (GPU) parallel computation, multiple-emitter fitting, and model recommendation via Bayesian Information Criterion (BIC). This method, called PALMER, exhibits satisfactory localization accuracy comparable with the previous reported SSM_BIC method, while executes more than two orders of magnitudes faster. Meanwhile, compared to the conventional localization microscopy which is based on sparse emitter localization, high-density localization microscopy based the PALMER method allows a speed gain of up to ~14-fold in obtaining a super-resolution image with the same Nyquist resolution.

NeuroGPS-Tree: automatic reconstruction of large-scale neuronal populations with dense neurites

The reconstruction of neuronal populations, a key step in understanding neural circuits, remains a challenge in the presence of densely packed neurites. Here we achieved automatic reconstruction of neuronal populations by partially mimicking human strategies to separate individual neurons. For populations not resolvable by other methods, we obtained recall and precision rates of approximately 80%. We also demonstrate the reconstruction of 960 neurons within 3 h.

NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model

Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.

Method to reconstruct neuronal action potential train from two-photon calcium imaging

Identification of a small population of neuronal action potentials (APs) firing is considered essential to discover the operating principles of neuronal circuits. A promising method is to indirectly monitor the AP discharges in neurons from the recordings their intracellular calcium fluorescence transients. However, it is hard to reveal the nonlinear relationship between neuronal calcium fluorescence transients and the corresponding AP burst discharging. We propose a method to reconstruct the neuronal AP train from calcium fluorescence diversifications based on a multiscale filter and a convolution operation. Results of experimental data processing show that the false-positive rate and the event detection rate are about 10 and 90%, respectively. Meanwhile, the APs firing at a high frequency up to 40 Hz can also be successfully identified. From the results, it can be concluded that the method has strong power to reconstruct a neuronal AP train from a burst firing.

Digital reconstruction of the cell body in dense neural circuits using a spherical-coordinated variational model

Mapping the neuronal circuits is essential to understand brain function. Recent technological advancements have made it possible to acquire the brain atlas at single cell resolution. Digital reconstruction of the neural circuits down to this level across the whole brain would significantly facilitate brain studies. However, automatic reconstruction of the dense neural connections from microscopic image still remains a challenge. Here we developed a spherical-coordinate based variational model to reconstruct the shape of the cell body i.e. soma, as one of the procedures for this purpose. When intuitively processing the volumetric images in the spherical coordinate system, the reconstruction of somas with variational model is no longer sensitive to the interference of the complicated neuronal morphology, and could automatically and robustly achieve accurate soma shape regardless of the dense spatial distribution, and diversity in cell size, and morphology. We believe this method would speed drawing the neural circuits and boost brain studies.

Identification of the direction of the neural network activation with a cellular resolution by fast two-photon imaging

Spatiotemporal activity patterns in local neural networks are fundamental to understanding how information is processed and stored in brain microcircuits. Currently, imaging techniques are able to map the directional activation of macronetworks across brain areas; however, these strategies still fail to resolve the activation direction for fine microcircuits with cellular spatial resolution. Here, we show the capability to identify the activation direction of a multicell network with a cellular resolution and millisecond precision by using fast two-photon microscopy and cross correlation procedures. As an example, we characterized a directional neuronal network in an epilepsy brain slice to provide different initiation delay among multiple neurons defined at a millisecond scale.

Reconstruction of burst activity from calcium imaging of neuronal population via Lq minimization and interval screening.

Calcium imaging is becoming an increasingly popular technology to indirectly measure activity patterns in local neuronal networks. Based on the dependence of calcium fluorescence on neuronal spiking, two-photon calcium imaging affords single-cell resolution of neuronal population activity. However, it is still difficult to reconstruct neuronal activity from complex calcium fluorescence traces, particularly for traces contaminated by noise. Here, we describe a robust and efficient neuronal-activity reconstruction method that utilizes Lq minimization and interval screening (IS), which we refer to as LqIS. The simulation results show that LqIS performs satisfactorily in terms of both accuracy and speed of reconstruction. Reconstruction of simulation and experimental data also shows that LqIS has advantages in terms of the recall rate, precision rate, and timing error. Finally, LqIS is demonstrated to effectively reconstruct neuronal burst activity from calcium fluorescence traces recorded from large-size neuronal population.