Scaling Computation and Memory in Living Cells

Temporally resolved transcriptional recording in E. coli DNA using a Retro-Cascorder

Biological signals occur over time in living cells. Yet most current approaches to interrogate biology, particularly gene expression, use destructive techniques that quantify signals only at a single point in time. A recent technological advance, termed the Retro-Cascorder, overcomes this limitation by molecularly logging a record of gene expression events in a temporally organized genomic ledger. The Retro-Cascorder works by converting a transcriptional event into a DNA barcode using a retron reverse transcriptase and then storing that event in a unidirectionally expanding clustered regularly interspaced short palindromic repeats (CRISPR) array via acquisition by CRISPR-Cas integrases. This CRISPR array-based ledger of gene expression can be retrieved at a later point in time by sequencing. Here we describe an implementation of the Retro-Cascorder in which the relative timing of transcriptional events from multiple promoters of interest is recorded chronologically in Escherichia coli populations over multiple days. We detail the molecular components required for this technology, provide a step-by-step guide to generate the recording and retrieve the data by Illumina sequencing, and give instructions for how to use custom software to infer the relative transcriptional timing from the sequencing data. The example recording is generated in 2 d, preparation of sequencing libraries and sequencing can be accomplished in 2-3 d, and analysis of data takes up to several hours. This protocol can be implemented by someone familiar with basic bacterial culture, molecular biology and bioinformatics. Analysis can be minimally run on a personal computer.

Molecular recording: transcriptional data collection into the genome

Advances in regenerative medicine depend upon understanding the complex transcriptional choreography that guides cellular development. Transcriptional molecular recorders, tools that record different transcriptional events into the genome of cells, hold promise to elucidate both the intensity and timing of transcriptional activity at single-cell resolution without requiring destructive multitime point assays. These technologies are dependent on DNA writers, which translate transcriptional signals into stable genomic mutations that encode the duration, intensity, and order of transcriptional events. In this review, we highlight recent progress toward more informative and multiplexable transcriptional recording through the use of three different types of DNA writing - recombineering, Cas1-Cas2 acquisition, and prime editing - and the architecture of the genomic data generated.

Recording gene expression order in DNA by CRISPR addition of retron barcodes

Biological processes depend on the differential expression of genes over time, but methods to make physical recordings of these processes are limited. Here we report a molecular system for making time-ordered recordings of transcriptional events into living genomes. We do this through engineered RNA barcodes, based on prokaryotic retrons1, that are reverse transcribed into DNA and integrated into the genome using the CRISPR-Cas system2. The unidirectional integration of barcodes by CRISPR integrases enables reconstruction of transcriptional event timing based on a physical record through simple, logical rules rather than relying on pretrained classifiers or post hoc inferential methods. For disambiguation in the field, we will refer to this system as a Retro-Cascorder.

A synthetic distributed genetic multi-bit counter

A design for genetically encoded counters is proposed via repressor-based circuits. An N-bit counter reads sequences of input pulses and displays the total number of pulses, modulo 2N. The design is based on distributed computation with specialized cell types allocated to specific tasks. This allows scalability and bypasses constraints on the maximal number of circuit genes per cell due to toxicity or failures due to resource limitations. The design starts with a single-bit counter. The N-bit counter is then obtained by interconnecting (using diffusible chemicals) a set of N single-bit counters and connector modules. An optimization framework is used to determine appropriate gate parameters and to compute bounds on admissible pulse widths and relaxation (inter-pulse) times, as well as to guide the construction of novel gates. This work can be viewed as a step toward obtaining circuits that are capable of finite automaton computation in analogy to digital central processing units.

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A single-bit counter is designed for a repressor-based genetic circuit

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A scalable multi-bit counter is enabled by distributing the design across cells

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A computational optimization framework is proposed to guide the design

Measurement of large serine integrase enzymatic characteristics in HEK293 cells reveals variability and influence on downstream reporter expression

Large serine integrases (LSIs) offer tremendous potential for rapid genetic engineering as well as building biological systems capable of responding to stimuli and integrating information. Currently, there is no unified metric for directly measuring the enzymatic characteristics of LSI function, which hinders evaluation of their suitability to specific applications. Here, we present an experimental protocol for recording DNA recombination in HEK293 cells in real-time through fluorophore expression and software which fits the kinetic data to a model tailored to LSI recombination dynamics. Our model captures the activity of LSIs as three parameters: expression level (K_exp ), catalytic rate (k_cat ), and substrate affinity (K_d ). The expression level and catalytic rate for phiC31 and Bxb1 varied greatly, suggesting disparate routes to high recombination efficiencies. Moreover, the expression level and substrate affinity jointly impacted downstream reporter expression, potentially by obstructing transcriptional machinery. We validated these observations by swapping between promoters and mutating key recombinase residues and DNA recognition sites to individually modulate each parameter. Our model for identifying key LSI parameters in cellulo provides insight into selecting the optimal recombinase for various applications as well as for guiding the engineering of improved LSIs.

Advancing CRISPR-Based Programmable Platforms beyond Genome Editing in Mammalian Cells

Human diseases are caused by dysregulation of cellular biological programs that are encoded in DNA. Unveiling the endogenous programs and encoding new programs into the genome are key to creating novel diagnostic and therapeutic strategies. CRISPR/Cas9, originally identified in bacteria, has revolutionized genome editing in mammalian cells. Recent advances in CRISPR technologies have provided new programmable platforms for modifying cell function and behavior. CRISPR-based transcriptional regulators and modified gRNAs have enabled multiplexed regulation and visualization of genome dynamics with spatiotemporal precision. Using these toolkits, genome-scale screening platforms can identify key genetic elements or combinations thereof that modulate phenotypes in mammalian cells. In addition, imaging platforms for multiplexed genomic labeling have been created to study the conformation and dynamics of chromatin in living cells, which are essential for genome function. Furthermore, CRISPR-based computation and memory platforms have been built in living mammalian cells by using DNA as a data processing and storage medium to regulate and monitor cellular behaviors. The conditional regulation of CRISPR-based parts has enabled the design of complex multilayered biological programs. CRISPR-based memory platforms can continuously record biological events as mutations in defined DNA loci. By making use of base editors, CRISPR-based computation and memory platforms have been interconnected to perform logic operations based on past events. These technologies open up new avenues for understanding biological phenomena and designing mammalian cells as living machines for biomedical applications.

A multiplexed, electrochemical interface for gene-circuit-based sensors

The field of synthetic biology has used the engineered assembly of synthetic gene networks to create a wide range of function in biological systems. As part of this work, gene circuit-based sensors have primarily used optical proteins (e.g. fluorescent, colorimetric) as reporter outputs, which has limited the potential to measure multiple distinct signals. Here we present an electrochemical interface that permits expanded multiplexed reporting for cell-free gene circuit-based sensors. We have engineered a scalable system of reporter enzymes that cleave specific DNA sequences in solution, which results in an electrochemical signal when these newly liberated strands are captured at the surface of a nanostructured microelectrode. We describe the development of this interface and show its utility using a ligand-inducible gene circuit and toehold switch-based sensors, including the detection of multiple antibiotic resistance genes in parallel. This technology has the potential to expand the field of synthetic biology by providing an interface with materials, hardware and software.

Creating the protein version of DNA base pairing

Programmable and modular protein-protein interactions designed from scratch

Synthetic Biology Goes Cell-Free

Cell-free systems (CFS) have recently evolved into key platforms for synthetic biology applications. Many synthetic biology tools have traditionally relied on cell-based systems, and while their adoption has shown great progress, the constraints inherent to the use of cellular hosts have limited their reach and scope. Cell-free systems, which can be thought of as programmable liquids, have removed many of these complexities and have brought about exciting opportunities for rational design and manipulation of biological systems. Here we review how these simple and accessible enzymatic systems are poised to accelerate the rate of advancement in synthetic biology and, more broadly, biotechnology.

What would a synthetic connectome look like?

A major challenge of contemporary neuroscience is to unravel the structure of the connectome, the ensemble of neural connections that link between different functional units of the brain, and to reveal how this structure relates to brain function. This thriving area of research largely follows the general tradition in biology of reverse-engineering, which consists of first observing and characterizing a biological system or process, and then deconstructing it into its fundamental building blocks in order to infer its modes of operation. However, a complementary form of biology has emerged, synthetic biology, which emphasizes construction-based forward-engineering. The synthetic biology approach comprises the assembly of new biological systems out of elementary biological parts. The rationale is that the act of building a system can be a powerful method for gaining deep understanding of how that system works. As the fields of connectomics and synthetic biology are independently growing, I propose to consider the benefits of combining the two, to create synthetic connectomics, a new form of neuroscience and a new form of synthetic biology. The goal of synthetic connectomics would be to artificially design and construct the connectomes of live behaving organisms. Synthetic connectomics could serve as a unifying platform for unraveling the complexities of brain operation and perhaps also for generating new forms of artificial life, and, in general, could provide a valuable opportunity for empirically exploring theoretical predictions about network function. What would a synthetic connectome look like? What purposes would it serve? How could it be constructed? This review delineates the novel notion of a synthetic connectome and aims to lay out the initial steps towards its implementation, contemplating its impact on science and society.

Nucleic Acid Databases and Molecular-Scale Computing

DNA outperforms most conventional storage media in terms of information retention time, physical density, and volumetric coding capacity. Advances in synthesis and sequencing technologies have enabled implementations of large synthetic DNA databases with impressive storage capacity and reliable data recovery. Several robust DNA storage architectures featuring random access, error correction, and content rewritability have been constructed with the potential for scalability and cost reduction. We survey these recent achievements and discuss alternative routes for overcoming the hurdles of engineering practical DNA storage systems. We also review recent exciting work on in vivo DNA memory including intracellular recorders constructed by programmable genome editing tools. Besides information storage, DNA could serve as a versatile molecular computing substrate. We highlight several state-of-the-art DNA computing techniques such as strand displacement, localized hybridization chain reactions, and enzymatic reaction networks. We summarize how these simple primitives have facilitated rational designs and implementations of in vitro DNA reaction networks that emulate digital/analog circuits, artificial neural networks, or nonlinear dynamic systems. We envision these modular primitives could be strategically adapted for sophisticated database operations and massively parallel computations on DNA databases. We also highlight in vivo DNA computing modules such as CRISPR logic gates for building scalable genetic circuits in living cells. To conclude, we discuss various implications and challenges of DNA-based storage and computing, and we particularly encourage innovative work on bridging these two areas of research to further explore molecular parallelism and near-data processing. Such integrated molecular systems could lead to far-reaching applications in biocomputing, security, and medicine.