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Cell signalling and communication are fundamental to living cellular communities. For the past two decades, there has been continuous development of bottom-up engineered synthetic cells, which have become more and more similar to their natural counterparts. However, we are only scratching the surface with the development of synthetic cellular communities and their integration into natural tissues. Here, we review different intercellular communication mechanisms engineered for synthetic cells and classify them based on their resemblance to natural cell signalling mechanisms: autocrine, paracrine, and juxtacrine. In particular, we highlight recent advances in molecular tools for intercellular communication designs and discuss potential applications of engineering synthetic cellular communities and synthetic cell-natural cell communication. With further advances in this area, synthetic cellular communities will be powerful tools for understanding and manipulating cellular functions, thus unlocking potential applications in biosensing, cellular reprogramming, and sustainability. 

Abstract Synthetic cells offer a versatile platform for addressing biomedical and environmental challenges, due to their modular design and capability to mimic cellular processes such as biosensing, intercellular communication, and metabolism. Constructing synthetic cells capable of stimuli‐responsive secretion is vital for applications in targeted drug delivery and biosensor development. Previous attempts at engineering secretion for synthetic cells have been confined to non‐specific cargo release via membrane pores, limiting the spatiotemporal precision and specificity necessary for selective secretion. Here, a protein‐based platform termed TEV Protease‐mediated Releasable Actin‐binding Protein (TRAP) is designed and constructed for selective, rapid, and triggerable secretion in synthetic cells. TRAP is designed to bind tightly to reconstituted actin networks and is proteolytically released from bound actin, followed by secretion via cell‐penetrating peptide membrane translocation. TRAP's efficacy in facilitating light‐activated secretion of both fluorescent and luminescent proteins is demonstrated. By equipping synthetic cells with a controlled secretion mechanism, TRAP paves the way for the development of stimuli‐responsive biomaterials, versatile synthetic cell‐based biosensing systems, and therapeutic applications through the integration of synthetic cells with living cells for targeted delivery of protein therapeutics. 

Cell signaling through direct physical cell–cell contacts plays vital roles in biology during development, angiogenesis, and immune response. Intercellular communication mechanisms between synthetic cells constructed from the bottom up are majorly reliant on diffusible chemical signals, thus limiting the range of responses in receiver cells. Engineering contact‐dependent signaling between synthetic cells promises to unlock more complicated signaling schemes with spatial responses. Herein, a light‐activated contact‐dependent communication scheme for synthetic cells is designed and demonstrated. A split luminescent protein is utilized to limit signal generation exclusively to contact interfaces of synthetic cells, driving the recruitment of a photoswitchable protein in receiver cells, akin to juxtacrine signaling in living cells. The modular design not only demonstrates contact‐dependent communication between synthetic cells but also provides a platform for engineering orthogonal contact‐dependent signaling mechanisms. 

We consider the well-studied problem of completing a rank- , -incoherent matrix from incomplete observations. We focus on this problem in the semi-random setting where each entry is independently revealed with probability at least . Whereas multiple nearly-linear time algorithms have been established in the more specialized fully-random setting where each entry is revealed with probablity exactly , the only known nearly-linear time algorithm in the semi-random setting is due to [CG18], whose sample complexity has a polynomial dependence on the inverse accuracy and condition number and thus cannot achieve high-accuracy recovery. Our main result is the first high-accuracy nearly-linear time algorithm for solving semi-random matrix completion, and an extension to the noisy observation setting. Our result builds upon the recent short-flat decomposition framework of [KLLST23a, KLLST23b] and leverages fast algorithms for flow problems on graphs to solve adaptive reweighting subproblems efficiently 

The authors provide the first tight sample complexity bounds for shadow tomography and classical shadows in the regime where the target error is below some sufficiently small inverse polynomial in the dimension of the Hilbert space. Specifically, they present a protocol that, given any 𝑚 ∈ 𝑁 m∈N and 𝜖 ≤ 𝑂 ( 𝑑 − 1 / 2 ) ϵ≤O(d −1/2 ), measures 𝑂 ( log ⁡ ( 𝑚 ) / 𝜖 2 ) O(log(m)/ϵ 2 ) copies of an unknown mixed state 𝜌 ∈ 𝐶 𝑑 × 𝑑 ρ∈C d×d and outputs a classical description of 𝜌 ρ. This description can then be used to estimate any collection of 𝑚 m observables to within additive accuracy 𝜖 ϵ. Previously, even for the simpler case of shadow tomography where observables are known in advance, the best known rates either scaled benignly but suboptimally in all of 𝑚 , 𝑑 , 𝜖 m,d,ϵ, or scaled optimally in 𝜖 , 𝑚 ϵ,m but included additional polynomial factors in 𝑑 d. Interestingly, the authors also show via dimensionality reduction that one can rescale 𝜖 ϵ and 𝑑 d to reduce to the regime where 𝜖 ≤ 𝑂 ( 𝑑 − 1 / 2 ) ϵ≤O(d −1/2 ). Their algorithm draws on representation-theoretic tools developed in the context of full state tomography.