The 10q26 Risk Haplotype of Age-Related Macular Degeneration Aggravates Subretinal Inflammation by Impairing Monocyte Elimination.

A minor haplotype of the 10q26 locus conveys the strongest genetic risk for age-related macular degeneration (AMD). Here, we examined the mechanisms underlying this susceptibility. We found that monocytes from homozygous carriers of the 10q26 AMD-risk haplotype expressed high amounts of the serine peptidase HTRA1, and HTRA1 located to mononuclear phagocytes (MPs) in eyes of non-carriers with AMD. HTRA1 induced the persistence of monocytes in the subretinal space and exacerbated pathogenic inflammation by hydrolyzing thrombospondin 1 (TSP1), which separated the two CD47-binding sites within TSP1 that are necessary for efficient CD47 activation. This HTRA1-induced inhibition of CD47 signaling induced the expression of pro-inflammatory osteopontin (OPN). OPN expression increased in early monocyte-derived macrophages in 10q26 risk carriers. In models of subretinal inflammation and AMD, OPN deletion or pharmacological inhibition reversed HTRA1-induced pathogenic MP persistence. Our findings argue for the therapeutic potential of CD47 agonists and OPN inhibitors for the treatment of AMD.

Streamlined digital bioassays with a 3D printed sample changer.

Droplet-based microfluidics has permeated many areas of life sciences including biochemistry, biology and medicine. Water-in-oil droplets act as independent femto- to nano-liter reservoirs, enabling the parallelization of (bio)chemical reactions with a minimum sample input. Among the range of applications spanned by droplet microfluidics, digital detection of biomolecules, using Poissonian isolation of single molecules in compartments, has gained considerable attention due to the high accuracy, sensitivity and robustness of these methods. However, while the droplet throughput can be very high, the sample throughput of these methods is poor in comparison to well plate-based assays. This limitation comes from the necessity to convert independently each sample into a monodisperse emulsion. In this paper, we report a versatile device that performs the quick sequential partitioning of up to 15 samples using a single microfluidic chip. A 3D printed sample rotor is loaded with all samples and connected to a pressure source. Simple magnetic actuation is then used to inject the samples in the microfluidic chip without pressure disruption. This procedure generates monodisperse droplets with high sample-to-sample consistency. We also describe a fluorescent barcoding strategy that allows all samples to be collected, incubated, imaged and analyzed simultaneously, thus decreasing significantly the time of the assay. As an example of application, we perform a droplet digital PCR assay for the quantification of a DNA amplicon from 8 samples in less than 2 hours. We further validate our approach demonstrating the parallel quantification of 11 microRNAs from a human sample using an isothermal nucleic acid amplification chemistry. As an off-chip device, the sample changer can be connected to a variety of microfluidic geometries and therefore, used for a wide range of applications.

Functional analysis of single enzymes combining programmable molecular circuits with droplet-based microfluidics.

The analysis of proteins at the single-molecule level reveals heterogeneous behaviours that are masked in ensemble-averaged techniques. The digital quantification of enzymes traditionally involves the observation and counting of single molecules partitioned into microcompartments via the conversion of a profluorescent substrate. This strategy, based on linear signal amplification, is limited to a few enzymes with sufficiently high turnover rate. Here we show that combining the sensitivity of an exponential molecular amplifier with the modularity of DNA-enzyme circuits and droplet readout makes it possible to specifically detect, at the single-molecule level, virtually any D(R)NA-related enzymatic activity. This strategy, denoted digital PUMA (Programmable Ultrasensitive Molecular Amplifier), is validated for more than a dozen different enzymes, including many with slow catalytic rate, and down to the extreme limit of apparent single turnover for Streptococcus pyogenes Cas9. Digital counting uniquely yields absolute molar quantification and reveals a large fraction of inactive catalysts in all tested commercial preparations. By monitoring the amplification reaction from single enzyme molecules in real time, we also extract the distribution of activity among the catalyst population, revealing alternative inactivation pathways under various stresses. Our approach dramatically expands the number of enzymes that can benefit from quantification and functional analysis at single-molecule resolution. We anticipate digital PUMA will serve as a versatile framework for accurate enzyme quantification in diagnosis or biotechnological applications. These digital assays may also be utilized to study the origin of protein functional heterogeneity.

In Vitro Enzyme Self-Selection Using Molecular Programs.

Directed evolution provides a powerful route for in vitro enzyme engineering. State-of-the-art techniques functionally screen up to millions of enzyme variants using high throughput microfluidic sorters, whose operation remains technically challenging. Alternatively, in vitro self-selection methods, analogous to in vivo complementation strategies, open the way to even higher throughputs, but have been demonstrated only for a few specific activities. Here, we leverage synthetic molecular networks to generalize in vitro compartmentalized self-selection processes. We introduce a programmable circuit architecture that can link an arbitrary target enzymatic activity to the replication of its encoding gene. Microencapsulation of a bacterial expression library with this autonomous selection circuit results in the single-step and screening-free enrichment of genetic sequences coding for programmed enzymatic phenotypes. We demonstrate the potential of this approach for the nicking enzyme Nt.BstNBI (NBI). We applied autonomous selection conditions to enrich for thermostability or catalytic efficiency, manipulating up to 107 microcompartments and 5 × 105 variants at once. Full gene reads of the libraries using nanopore sequencing revealed detailed mutational activity landscapes, suggesting a key role of electrostatic interactions with DNA in the enzyme's turnover. The most beneficial mutations, identified after a single round of self-selection, provided variants with, respectively, 20 times and 3 °C increased activity and thermostability. Based on a modular molecular programming architecture, this approach does not require complex instrumentation and can be repurposed for other enzymes, including those that are not related to DNA chemistry.

Accurate gene consensus at low nanopore coverage.

BACKGROUND: Nanopore technologies allow high-throughput sequencing of long strands of DNA at the cost of a relatively large error rate. This limits its use in the reading of amplicon libraries in which there are only a few mutations per variant and therefore they are easily confused with the sequencing noise. Consensus calling strategies reduce the error but sacrifice part of the throughput on reading typically 30 to 100 times each member of the library. FINDINGS: In this work, we introduce SINGLe (SNPs In Nanopore reads of Gene Libraries), an error correction method to reduce the noise in nanopore reads of amplicons containing point variations. SINGLe exploits that in an amplicon library, all reads are very similar to a wild-type sequence from which it is possible to experimentally characterize the position-specific systematic sequencing error pattern. Then, it uses this information to reweight the confidence given to nucleotides that do not match the wild-type in individual variant reads and incorporates it on the consensus calculation. CONCLUSIONS: We tested SINGLe in a mutagenic library of the KlenTaq polymerase gene, where the true mutation rate was below the sequencing noise. We observed that contrary to other methods, SINGLe compensates for the systematic errors made by the basecallers. Consequently, SINGLe converges to the true sequence using as little as 5 reads per variant, fewer than the other available methods.

Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols.

High-throughput, in vitro approaches for the evolution of enzymes rely on a random micro-encapsulation to link phenotypes to genotypes, followed by screening or selection steps. In order to optimise these approaches, or compare one to another, one needs a measure of their performance at extracting the best variants of a library. Here, we introduce a new metric, the Selection Quality Index (SQI), which can be computed from a simple mock experiment, performed with a known initial fraction of active variants. In contrast to previous approaches, our index integrates the effect of random co-encapsulation, and comes with a straightforward experimental interpretation. We further show how this new metric can be used to extract general protocol efficiency trends or reveal hidden selection mechanisms such as a counterintuitive form of beneficial poisoning in the compartmentalized self-replication protocol.