Engineering Gelation Kinetics in Living Silk Hydrogels by Differential Dynamic Microscopy Microrheology and Machine Learning

Abstract

Microbes embedded in hydrogels comprise one form of living material. Discovering formulations that balance potentially competing for mechanical and biological properties in living hydrogels—for example, gel time of the hydrogel formulation and viability of the embedded organisms—can be challenging. In this study, a pipeline is developed to automate the characterization of the gel time of hydrogel formulations. Using this pipeline, living materials comprised of enzymatically crosslinked silk and embedded E. coli—formulated from within a 4D parameter space—are engineered to gel within a pre‐selected timeframe. Gelation time is estimated using a novel adaptation of microrheology analysis using differential dynamic microscopy (DDM). In order to expedite the discovery of gelation regime boundaries, Bayesian machine learning models are deployed with optimal decision‐making under uncertainty. The rate of learning is observed to vary between artificial intelligence (AI)‐assisted planning and human planning, with the fastest rate occurring during AI‐assisted planning following a round of human planning. For a subset of formulations gelling within a targeted timeframe of 5–15 min, fluorophore production within the embedded cells is substantially similar across treatments, evidencing that gel time can be tuned independent of other material properties—at least over a finite range—while maintaining biological activity.

Differential dynamic microscopy for the characterization of polymer systems

“…Besides providing a convenient and accessible alternative to traditional light scattering methods, DDM opens up a number of exciting and largely unexplored possibilities, leveraging its unique combination of features. For example, its intrinsic user‐independence, the compatibility with different imaging modalities and the robustness against optical imperfections and multiple scattering make DDM an ideal candidate for the integration in automated platforms, combining sample preparation, microscopy, quantitative image analysis and machine learning, performing high‐throughput characterization of polymeric materials with complex composition and design spaces 33 . In general, the fact that DDM is compatible with a variety of imaging modes, can be exploited to simplify the design of experiments aimed at simultaneously monitoring different components within a given system.…”

“…For example, using Brownian probe measurements on a series of crosslinking polyacrylamide solutions, DDM microrheology was used to verify the concept of time‐cure superposition 31,32 and its use in precise estimation of critical gelation exponents and the gel point 27 . More recently, such experiments were implemented in a novel combination of automated sample preparation, microscopy, DDM analysis and machine learning to perform high‐throughput screening of gelation kinetics of silk fibroin biopolymer networks over a wide, multi‐component compositional space in order to identify compositional windows with desirable gelation times 33 . Such integrated measurements and methods involving DDM microrheology show significant promise for the future application of DDM microrheology to polymeric materials with complex composition and design spaces.…”

Passive and Active Microrheology for Biomedical Systems

“…DDM does not require user input. Taking this advantage, Martineau et al built an automated system with DDM and machine learning algorithms to specify hydrogel formulation ( Martineau et al, 2022 ). One can envision utilizing machine-learning algorithms taking microrheological probe trajectories of ex vivo or in vivo tissue as an input, and controlling a 3D bioprinter to replicate organ tissue for patient transplant, disease research, and natural bioproduct production, potentially minimizing the need for organ donations ( Abouna, 2008 ).…”

Differential dynamic microscopy for the characterisation of motility in biological systems

“…The data shown above was measured using phase contrast illumination. However, DDM can be carried out using a wide range of illumination conditions including brightfield, 32,36 phase contrast, 16,37,38 fluorescence, 32,36,37 differential interference contrast, 39 polarised 40 and darkfield. [41][42][43] In addition, DDM can be applied to confocal fluorescence imaging (ConDDM).…”