Generating and Co-culturing Murine Primary Microglia and Cortical Neurons.

Microglia are tissue-resident macrophages of the central nervous system (CNS), performing numerous functions that support neuronal health and CNS homeostasis. They are a major population of immune cells associated with CNS disease activity, adopting reactive phenotypes that potentially contribute to neuronal injury during chronic neurodegenerative diseases such as multiple sclerosis (MS). The distinct mechanisms by which microglia regulate neuronal function and survival during health and disease remain limited due to challenges in resolving the complex in vivo interactions between microglia, neurons, and other CNS environmental factors. Thus, the in vitro approach of co-culturing microglia and neurons remains a valuable tool for studying microglia-neuronal interactions. Here, we present a protocol to generate and co-culture primary microglia and neurons from mice. Specifically, microglia were isolated after 9-10 days in vitro from a mixed glia culture established from brain homogenates derived from neonatal mice between post-natal days 0-2. Neuronal cells were isolated from brain cortices of mouse embryos between embryonic days 16-18. After 4-5 days in vitro, neuronal cells were seeded in 96-well plates, followed by the addition of microglia to form the co-culture. Careful timing is critical for this protocol as both cell types need to reach experimental maturity to establish the co-culture. Overall, this co-culture can be useful for studying microglia-neuron interactions and can provide multiple readouts, including immunofluorescence microscopy, live imaging, as well as RNA and protein assays.

How well can fine balance work for covariate balancing.

Fine balance is a matching technique to improve covariate balance in observational studies. It constrains a match to have identical distributions for some covariates without restricting who is matched to whom. However, despite its wide application and excellent performance in practice, there is very little theory indicating when the method is likely to succeed or fail and to what extent it can remove covariate imbalance. In order to answer these questions, this paper studies the limits of what is possible for covariate balancing using fine balance and near-fine balance. The investigations suggest that given the distributions of the treated and control groups, in large samples, the maximum achievable balance by using fine balance only depends on the matching ratio (ie, the ratio of the sample size of the control group to that of the treated group). In addition, the results indicate how to estimate this matching ratio threshold without knowledge of the true distributions in finite samples. The findings are also illustrated by numerical studies in this paper.

Evaluating and improving a matched comparison of antidepressants and bone density.

Matching is a common approach to covariate adjustment in estimating causal effects in observational studies. It is important to assess covariate balance of the matched samples. This is usually done informally, in ways that have a number of limitations. First, there are many diagnostics, even if covariates are assessed one at a time, which raises multiplicity issues. In addition, joint distributions of covariates, even bivariate distributions, are often ignored. Further, it is an open question whether diagnostics identify the major problems. To address these issues, a formal assessment of covariate balance is developed in the current paper. Unlike the common informal diagnostics, the proposed method compares both marginal distributions and joint distributions of the matched sample with those of the benchmark, complete randomizations. The method controls the probability of falsely identifying a covariate imbalance among many comparisons, yet it has a high probability of correctly detecting and identifying a major problem. An R package met implementing the proposed method is available on CRAN.

Directional penalties for optimal matching in observational studies.

Multivariate matching in observational studies tends to view covariate differences symmetrically: a difference in age of 10 years is thought equally problematic whether the treated subject is older or younger than the matched control. If matching is correcting an imbalance in age, such that treated subjects are typically older than controls, then the situation in need of correction is asymmetric: a matched pair with a difference in age of 10 years is much more likely to have an older treated subject and a younger control than the opposite. Correcting the bias may be easier if matching tries to avoid the typical case that creates the bias. We describe several easily used, asymmetric, directional penalties and illustrate how they can improve covariate balance in a matched sample. The investigator starts with a matched sample built in a conventional way, then diagnoses residual covariate imbalances in need of reduction, and achieves the needed reduction by slightly altering the distance matrix with directional penalties, creating a new matched sample. Unlike penalties commonly used in matching, a directional penalty can go too far, reversing the direction of the bias rather than reducing the bias, so the magnitude of the directional penalty matters and may need adjustment. Our experience is that two or three adjustments, guided by balance diagnostics, can substantially improve covariate balance, perhaps requiring fifteen minutes effort sitting at the computer. We also explore the connection between directional penalties and a widely used technique in integer programming, namely Lagrangian relaxation of problematic linear side constraints in a minimum cost flow problem. In effect, many directional penalties are Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. The method and example are in an R package DiPs at CRAN.