Thanks to the advancements in micro- and nanomechatronics techniques, the capability to inject foreign materials into target locations inside biosamples has become increasingly crucial. However, technical problems, like orientation and penetration, make manual microinjection challenging, while most available automated platforms are too targeted and often fail to support reconfiguration for different injection tasks. In this article, we first review the state-of-the-art in microinjection techniques for biomedical applications. Then, to solve the aforementioned problems, we report an autonomous omnidirectional microinjection (AOM) system based on effortless updates of manual injection hardware with experiments. Future perspectives on the system are outlined with the possibility of multiplying daily genetic screens and the potential of widespread usage in laboratories and clinics.

The human fingertip is an exquisitely powerful bio-tactile sensor in perceiving different materials based on various highly-sensitive mechanoreceptors distributed all over the skin. The tactile perception of surface roughness and material hardness can be estimated by skin vibrations generated during a fingertip stroking of a surface instead of being maintained in a static position. Moreover, reciprocating sliding with increasing velocities and pressures are two common behaviors in humans to discriminate different materials, but the question remains as to what the correlation of the sliding velocity and normal load on the tactile perceptions of surface roughness and hardness is for material discrimination. In order to investigate this correlation, a finger-inspired crossed-I beam structure tactile tester has been designed to mimic the anthropic tactile discrimination behaviors. A novel method of characterizing the fast Fourier transform integral (FFT) slope of the vibration acceleration signal generated from fingertip rubbing on surfaces at increasing sliding velocity and normal load, respectively, are defined as k_v and k_w, and is proposed to discriminate the surface roughness and hardness of different materials. Over eight types of materials were tested, and they proved the capability and advantages of this high tactile-discriminating method. Our study may find applications in investigating humanoid robot perceptual abilities.

The zebrafish has been proven to be a significant model organism in various research fields. For investigating the in vivo properties of biologics within zebrafish with developed organs, an automated zebrafish larva organ injection system is crucially needed. However, current zebrafish larva manipulation techniques cannot accomplish this operation efficiently and continuously. Methods: In this paper, we present a novel zebrafish larva manipulation technique with two key steps in the microinjection system: orienting and aspirating zebrafish larvae automatically. The orientation control is realized in a customized microfluidic chip, after which the larva moves tailfirst until reaching the channel exit. Then a dynamic model of larva aspiration is established and an adaptive robust controller is designed. Results: Experimental results demonstrate that high success rate can be reached and damage to larva body is reduced. Conclusion: The presented strategy is capable of orienting and aspirating zebrafish larvae smoothly and efficiently. Significance: The proposed methods have the advantage of low cost, easy implementability and good stability.

This article presents an automated strategy to touch the injection site on zebrafish larva skin with the injection pipette tip accurately in the presence of water-depth variation, which is a crucial problem to automate zebrafish larva microinjection. The presented method consists of two parts: adaptive coordinate transformation and curve evolution for edge detection. In the first part, the impact of refraction is taken into consideration. An adaptive calibration method is developed, which enables the coordinate transformation matrix to adapt to the changing water depth. In the second part, the abovementioned calibration result is used to keep the injection pipette tip descending along the desired route. A curve-evolution-based edge detection algorithm is introduced to detect the deformation of larva skin caused by contact with the injection pipette tip. Experimental results demonstrate that high accuracy and success rates are achieved. The effect of uncertainties caused by water-depth variation and the skill requirement in manual manipulation are eliminated. The proposed contact detection strategy can be extended to microinjection for other organisms. Note to Practitioners —As a typical multicellular model organism, the zebrafish has been increasingly used in biological research. For studying drug toxicity and disease models, exogenous substances need to be injected into zebrafish larvae. However, for both manual and automated injection, a fatal problem is that the camera on the microscope only provides 2-D positional information. It is laborious to align the pipette tip with the injection site along the $z$ -axis. Moreover, due to the characteristic of stereomicroscopes, the impact of refraction at the water surface cannot be ignored. In order to address these issues, in this article, we present an adaptive calibration method and an edge detection algorithm for zebrafish larva heart injection to avoid contact failure in practical implementations.

Organoids have emerged as powerful models for recapitulating tissue physiology and pathology in biomedical research. However, the need for consistent and complex manufacturing of organoids remains a challenge. The absence of standardization and quality control of cells dispersed within extracellular matrices impedes the widespread application of organoids. Here, we introduce a micromanipulation platform that effectively constrains cell distribution to enable patterned and complex organoid biofabrication. This system integrates multidimensional cell sense technology to provide real-time feedback, enabling adaptive fluid dynamics control that robustly compensates for seeding-induced nonlinear fluidic perturbations. Through this closed-loop control, the micromanipulation platform effectively restrains the spatial distribution of multiple cell types, promoting consistent self-assembly. Using this platform, we have successfully achieved programmable organoid manufacturing as well as the construction of assembloids composed of multiple cell types that better mimic mature organ microenvironments. This supports advancements in organoid-based biomechanism discovery, drug screening, and other organoid-related investigations.